How to Hire a Backend Developer (FastAPI)

Q: In FastAPI, which decorator is used to define a route that handles GET requests?

Correct answer: @app.get. FastAPI uses the @app.get decorator to define endpoints that handle GET requests.

Q: In a high-traffic production environment, which approach would be most effective for handling concurrent requests in a FastAPI application?

Correct answer: Use asynchronous endpoints with async def and run Uvicorn with multiple workers. Using asynchronous endpoints along with deploying Uvicorn with multiple workers ensures efficient handling of concurrent requests and better scalability in production environments.

Q: When deploying a FastAPI application in a high-traffic production environment, which deployment strategy is best suited to handle a large number of concurrent connections?

Correct answer: Deploy with Gunicorn using Uvicorn workers configured for asynchronous processing. Using Gunicorn with Uvicorn workers leverages asynchronous processing, which is essential for handling many concurrent connections in production environments.

Q: Walk me through what happens when a JSON POST request hits a FastAPI endpoint, from Uvicorn receiving it to the response being sent.

Why it matters: Separates candidates who understand the ASGI server, routing, dependency resolution, and Pydantic validation from those who only memorized decorators. What to listen for: Green flags: Uvicorn passing the ASGI request to the app, the router matching the path and method, dependencies being resolved, the body parsed and validated into the Pydantic model (422 on failure), the function running, and the return value filtered through response_model and serialized to JSON. Red flags: cannot explain where the 422 comes from or treats FastAPI as magic.

Q: Tell me about the most serious production incident you handled in a FastAPI service. How did you find the root cause, and what did you change afterwards?

Why it matters: Mid-level engineers are expected to own services operationally, not just write endpoints. What to listen for: Green flags: concrete signals (p99 latency, event-loop lag, SQLAlchemy pool checkout timeouts, worker restarts), tools such as py-spy, Sentry traces, or OpenTelemetry spans, a quick mitigation followed by a durable fix (e.g., replacing a sync SDK with an async client or moving it to the threadpool), and a blameless post-mortem with action items. Red flags: blaming others, no data, or a 'fix' that only added more Uvicorn workers.

Q: Tell me about the most significant architectural decision you drove on a Python API platform. What alternatives did you reject, and how did it play out?

Why it matters: Seniors are hired for judgment; the quality of their trade-off reasoning matters more than the outcome itself. What to listen for: Green flags: explicit alternatives with criteria (e.g., async SQLAlchemy versus sync sessions in the threadpool, Celery versus ARQ versus a managed queue like SQS), a written ADR, load-test or production metrics before and after, and honest discussion of what they would change. Red flags: resume-driven choices ('async because it's faster', 'microservices because it's modern') or no measurement of outcomes.

Q: If you need to validate incoming JSON data in a FastAPI endpoint, which tool should you use?

Correct answer: Pydantic models. FastAPI leverages Pydantic for request data validation, making it simple and effective.

Q: When validating and parsing complex query parameters in a FastAPI project, which approach provides the most robust real-world solution?

Correct answer: Integrate Pydantic models directly into query parameters. Integrating Pydantic models directly into query parameters leverages FastAPI's strong validation features, ensuring that the data is correctly parsed and validated, which is essential in real-world applications.

Q: You're implementing a background task in FastAPI to send emails asynchronously after an API call. Which approach offers the most robust and production-ready solution?

Correct answer: Integrate Celery with FastAPI to offload email sending to a dedicated worker process. Integrating Celery allows email tasks to be processed independently from the request-response cycle, ensuring reliability and scalability in production.

Q: Tell me about a bug in one of your Python projects that took you more than a day to solve. How did you eventually find it?

Why it matters: Juniors will often be stuck; how they investigate and when they escalate determines team productivity. What to listen for: Green flags: reading the full traceback from the bottom up, reproducing the bug in a failing pytest, using breakpoint() or the IDE debugger, logging the SQL emitted with echo=True on the engine, and asking for help with a clear summary of what was tried. Red flags: random code changes until it works, or never asking for help.

Hiring Workflow, Step by Step

Every stage of this hire can run in SkillSauce on one board, from the first applicant to the offer. The level sections below add the detail for junior (0-3 years), mid-level (3-6 years) and senior (6+ years) hires.

1
Open a Job Profile for the role
Create a Job Profile for the role: one Kanban board, free on every plan, that holds every candidate, test and interview for this hire. Write the job description with the free Job Description Generator, or start from the template in your level below.
2
Fill the candidate pool
Publish the job on your Career Page and share the link on LinkedIn, X and job boards, so every applicant lands on the board. To reach people who have not applied, search Candidate Lookup by domain and seniority, then narrow by industry, company and location. You can also import LinkedIn profiles by URL, upload resumes in bulk or import a CSV.
3
Shortlist the strongest resumes
Move applicants into the Resume Shortlister: TESSA AI scores every resume against the Job Profile and shows its reasoning, so you can see why each candidate made the cut before you send a single test.
4
Test the skills the role needs
Send the shortlist an IT assessment on Python 3.11+, FastAPI and Pydantic v2, with a coding test in any of 60+ languages. Build it from the Question Library of 320,000+ questions, or let TESSA AI generate it when your exact skill mix is missing, and keep AI proctoring on for remote candidates.
5
Interview with TESSA AI
Invite the candidates who pass to a TESSA AI interview on the role itself, in any of 100+ languages. Add a Culture Fit round to see how they work with a team like yours, and a Behavioural round for senior hires, where judgment and ownership matter most.
6
Meet the shortlist
Schedule the live technical round on Zoom or Google Meet with Google Calendar invites, and keep candidates posted on WhatsApp or email.
7
Decide together and make the offer
Compare finalists on their Reports: scored results for each test and AI interview, plus the proctoring activity of proctored tests. Collect your hiring team’s feedback on each candidate, then make the offer with the email templates in your level below.

Company Fit: Startups, SMBs and Enterprises

Startups

Startups pick FastAPI to ship typed, self-documenting APIs quickly — often in front of ML models or LLM features — without the shortcuts that collapse once real traffic arrives. SkillSauce helps founders hire FastAPI engineers who can go from an empty repo to a tested, Dockerized, OpenAPI-documented service in days, without burning founder time on manual technical screens.

30–45 minute FastAPI practical assessments that verify candidates can ship validated, tested endpoints with Pydantic v2 and pytest from a fresh project.
TESSA AI interviews that screen for ownership and speed, so founders only spend live interview time on the strongest applicants.
Assessment scenarios built around MVP realities: OAuth2/JWT auth, Alembic migrations, a background job, and a Dockerized deploy to a single cloud environment.
Start free: the Starter plan includes 40 credits, about 10 AI interviews or assessments, with no time limit and no card needed.

SMBs

Growing companies run a handful of FastAPI services that have started to strain: blocking calls hidden in async endpoints, lazy-loading surprises in async SQLAlchemy, background tasks lost on deploy, and inconsistent auth.

Performance-focused assessments that objectively measure event-loop blocking, N+1 query elimination, and JWT scope enforcement through automated SQL-count, concurrency, and security grading.
Standardized scoring rubrics so every hiring manager evaluates FastAPI candidates the same way.
TESSA AI interviews that check production ownership: incident response, OpenAPI contract evolution, and code review habits.
Turnkey onboarding signals: assessment reports highlight each hire's strengths and gaps to shape their first 90-day plan.

Enterprises

Enterprises and regulated industries (fintech, healthcare, insurance) increasingly standardize on FastAPI for internal platforms and AI-serving APIs, where security, consistency, and auditability are non-negotiable. SkillSauce gives them a globally consistent, defensible way to benchmark Python architects and engineers across regions, business units, and vendors.

Architecture-grade senior assessments covering the transactional outbox, idempotent webhooks, connection budgeting with PgBouncer, and Kubernetes graceful shutdown with chaos-tested grading.
Audit-ready evaluation trails with timestamped scoring, rubric versions, and proctoring evidence for compliance and fair-hiring reviews.
Consistent global benchmarking for in-house teams and outsourcing partners, using the same FastAPI rubric in every geography.
Custom assessment libraries aligned to internal platform standards such as a shared FastAPI service template, OIDC integration, and security baselines.
Evidence in one place: scored reports for every test and AI interview, with your hiring team’s feedback beside them.

Junior level

Junior Backend Developer (FastAPI)

Experience: 0 - 3 Years
Average time to hire: 10 - 18 Days

Core tech stack

Python 3.11+ (type hints, async/await)
FastAPI (path operations, APIRouter, Depends)
Pydantic v2
SQLAlchemy 2.0 / SQLModel
Uvicorn
pytest & TestClient

How to Hire a Junior Backend Developer (FastAPI)

Hiring a junior FastAPI developer is about verifying that the candidate understands what FastAPI derives from Python type hints rather than copy-pasting tutorial snippets. At 0–3 years, look for modern Python fundamentals (type hints, Annotated, f-strings, basic async/await), the ability to build a small CRUD slice with APIRouter, separate Pydantic v2 request and response models (ItemCreate vs ItemRead), a database session provided through Depends with a yield dependency, correct status codes with HTTPException, and the habit of writing pytest tests with TestClient. Framework trivia matters less than the ability to read a 422 Unprocessable Entity body, find the failing field in its loc array, and fix the schema.

1
Define a Realistic Junior Scope
Write down the slice a junior will own in their first quarter: CRUD path operations in an existing FastAPI service organized with APIRouter modules, Pydantic v2 schemas with Field constraints, a SQLAlchemy 2.0 session injected via Depends(get_db), Alembic migrations written with a reviewer, and pytest coverage. Explicitly exclude Celery/ARQ ownership, Kubernetes, and authentication architecture so the ad does not scare off strong graduates.
2
Source From Python Learning Communities
Target bootcamp and university graduates who have shipped a FastAPI portfolio project — a JWT-secured todo API, a URL shortener with SQLite, or a fork of the official full-stack-fastapi-template with meaningful changes. Python Discord, r/FastAPI, and GitHub forks of the template surface self-motivated learners who have already debugged a real request lifecycle. Beyond applicants, Candidate Lookup finds people by domain and seniority, narrowed by industry, company and location, and you can add them to the same pipeline.
3
Screen With a 30-Minute SkillSauce Practical Assessment
Send a SkillSauce task built on a pre-scaffolded FastAPI project with SQLAlchemy and SQLite: add POST /items and GET /items/{item_id}, return 201 on create and 404 for unknown IDs, enforce Field(min_length=1, gt=0) validation, use a response_model that hides internal columns, and make the provided pytest + TestClient suite pass. Auto-graded tests remove reviewer bias and filter out candidates who cannot run pytest locally. Build it in IT Assessments from the Question Library, add a coding test in the language you hire for, and keep AI proctoring on.
4
Run a 15-Minute TESSA AI Interview
Use TESSA to check communication and learning habits: how the candidate would investigate a 422 response from the frontend team, how they explain what Depends does to a non-Python teammate, and how they ask for help after being stuck for 30 minutes on an import error or a failing migration. TESSA runs this AI interview in any of 100+ languages and returns a scored report you can share with the team.
5
Hold a 45-Minute Pair-Debugging Session
Hand the candidate a broken FastAPI app: a router module that was never registered with app.include_router, an async def endpoint calling time.sleep() and requests.get(), a Pydantic v1 leftover (class Config: orm_mode = True and .dict()) failing under Pydantic v2, and a UserRead schema that leaks hashed_password. Observe how they use the /docs Swagger UI, the Uvicorn --reload log, and a debugger to verify each fix with a test.
6
Extend an Offer With a Mentorship Plan
Close within 10–18 days with a written 90-day plan, a named senior Python mentor, and an explicit learning budget (courses on async Python, SQLAlchemy 2.0, and Docker). Junior Python candidates are also courted by data and ML teams, so speed and a clear growth path toward async and distributed work win.

Pro Tips

Ask what happens when a path parameter declared as item_id: int receives 'abc'. Strong juniors explain that FastAPI validates it with Pydantic and returns a 422 whose detail[0].loc is ['path', 'item_id'], and that the same type hints drive the OpenAPI schema shown at /docs.
Probe def versus async def path operations. Good answers note that FastAPI runs plain def endpoints in a threadpool, while calling time.sleep() or the synchronous requests library inside async def blocks the event loop for every request handled by that worker.
Check Pydantic v2 vocabulary: model_dump() instead of .dict(), model_config = ConfigDict(from_attributes=True) instead of orm_mode, and @field_validator instead of @validator. Candidates who only know v1 idioms are coachable but need a ramp plan.
Ask why a project has separate UserCreate and UserRead schemas. Listen for: the create schema accepts a plain password, the read schema omits it, and response_model filters the returned ORM object so hashed_password can never leak even if it is loaded.

Junior Backend Developer (FastAPI) Job Description Template

Use this template to attract early-career Python engineers who have built at least one FastAPI service and are ready to ship typed, tested endpoints under close mentorship.

Junior Backend Developer (FastAPI) Job Description

Role Overview

We are hiring a Junior Backend Developer (FastAPI) to join our API team building typed REST services on Python 3.11 and FastAPI. You will ship well-tested path operations in an existing service, model requests and responses with Pydantic v2, work with SQLAlchemy 2.0 against PostgreSQL, and learn production practices — Alembic migrations, Docker, CI pipelines, structured logging — from senior engineers who review every pull request.

First 90 Days Deliverables

Days 1–30: Run the service locally with uvicorn app.main:app --reload and Docker Compose (PostgreSQL), learn the routers/, schemas/, and models/ layout, and close 3–5 starter tickets such as adding a validated field to a Pydantic schema, fixing an endpoint that returns 200 instead of 201, or adding a filter query parameter with Query(max_length=50).
Days 31–60: Deliver a complete CRUD feature as a new APIRouter (path operations, Pydantic Create/Update/Read schemas, SQLAlchemy model, and an Alembic migration generated with alembic revision --autogenerate and reviewed by hand), plus a pytest suite using TestClient and app.dependency_overrides with 80%+ coverage on new code.
Days 61–90: Register a custom exception handler with @app.exception_handler for a new domain error, add a BackgroundTasks job that writes an audit log after the response, enrich the endpoint docstrings and summary/responses metadata so the generated OpenAPI docs are client-ready, and deploy the feature to staging through the GitHub Actions pipeline.

Must-Have Requirements

Solid Python 3.10+ fundamentals: type hints, dataclasses, comprehensions, exceptions, virtual environments (venv, Poetry, or uv).
Hands-on experience building at least one FastAPI application with path, query, and body parameters.
Working knowledge of Pydantic models for request validation and response serialization.
SQL basics (joins, primary/foreign keys, indexes) and an ORM such as SQLAlchemy or SQLModel.
Writing tests with pytest and FastAPI's TestClient.
Comfort with Git branching, pull requests, and code review feedback.

Nice-to-Have Skills

Basic async/await understanding and awareness of when not to block the event loop.
Docker and Docker Compose for local dependencies.
Linting and typing tools: Ruff, mypy or Pyright, pre-commit.
Familiarity with JWT authentication using OAuth2PasswordBearer.

Benefits & Mentorship

Dedicated senior Python mentor with weekly 1:1s and pairing sessions.
Annual learning budget for courses, books, and PyCon tickets.
Competitive junior salary, health coverage, and a documented promotion rubric to Mid-Level within 18–24 months.

Need a version for another role or tone? Draft one with the free Job Description Generator.

Sourcing Channels for Early-Career FastAPI Talent

Junior FastAPI developers gather where modern Python is learned in public: beginner-friendly Discord servers, framework subreddits, and GitHub forks of the official project templates.

SkillSauce

Candidate Lookup

Find people who have not applied yet. Search by domain and seniority, narrow by industry, company and location, and add them to the role’s Job Profile with their available contact details.

SkillSauce

Career Page and LinkedIn Import

Publish the job on your Career Page and share the link on LinkedIn, X and job boards: every applicant lands in the role’s Job Profile. Import a LinkedIn profile by its URL when you spot someone.

Social network

X (Twitter) (opens in a new tab)

x.com

Post the role with hashtags such as #Python and #FastAPI, and join the X Communities where people in the field share their work.

Developer Community

Python Discord (opens in a new tab)

pythondiscord.com

One of the largest Python communities, with dedicated help channels where early-career developers ask and answer web framework questions. Look for members who consistently help others untangle Pydantic validation errors or async mistakes, and who showcase FastAPI projects in the community channels.

Social Community

r/FastAPI Subreddit (opens in a new tab)

reddit.com

A focused community where developers share FastAPI side projects, project-structure questions, and deployment write-ups. Engage with well-documented project showcases rather than cold-spamming, and lead with your mentorship program.

Open Source Directory

GitHub full-stack-fastapi-template Forks (opens in a new tab)

github.com

Many learners fork the official fastapi/full-stack-fastapi-template (FastAPI, SQLModel, PostgreSQL, Docker Compose). Review forks with meaningful commits — new routers, added tests, Alembic migrations — to find juniors who go beyond running the template.

Job Board

Python.org Job Board (opens in a new tab)

python.org

The Python Software Foundation's free job board is read by Python-first developers, including recent graduates. Post a concise listing that names FastAPI, Pydantic, and pytest, and highlight your practical, no-whiteboard process.

Recruitment Platform

LinkedIn Boolean Search – Early-Career FastAPI Talent (opens in a new tab)

linkedin.com

Use: ('FastAPI') AND ('Python') AND ('Pydantic' OR 'SQLAlchemy' OR 'SQLModel') AND ('pytest' OR 'REST API') AND ('Graduate' OR 'Junior' OR 'Associate' OR 'Intern'). Filter for 0–3 years and recent bootcamp or CS graduates with a linked GitHub profile.

Developer community

Stack Overflow (opens in a new tab)

stackoverflow.com

Find people who answer questions in the tags your stack uses: their answers show how they reason and explain.

Job boards

Job Boards

Post the job on Indeed and Glassdoor, and on Naukri and foundit for hiring in India. Where a board allows it, use your Career Page link as the apply link, so applicants land in the same Job Profile.

Campus and referrals

Campus Placement Cells and Referrals

Partner with college placement cells and career services for graduates and interns, and ask your team who they studied or interned with.

Early-career Python developers respond to outreach that promises real tickets, a real mentor, and a short practical evaluation instead of algorithm puzzles.

LinkedIn Post

We're hiring a Junior Backend Developer (FastAPI)! Built your first FastAPI service and want to see how typed Python APIs run in production? Join our API team and ship real endpoints from week one — with a senior mentor reviewing every PR. Stack: Python 3.11, FastAPI, Pydantic v2, SQLAlchemy 2.0, PostgreSQL, pytest, Docker What you'll learn: clean router/schema/model structure, Alembic migrations, dependency injection, async Python done right, CI/CD to staging Support: weekly 1:1s, learning budget, clear promotion path No whiteboard puzzles. Start with a 30-minute hands-on SkillSauce challenge: [SkillSauce Assessment Link] #FastAPI #Python #JuniorDeveloper #BackendDeveloper #Hiring #EntryLevelJobs

Show template

Twitter / X Post

Show template

Shipped a FastAPI project and know why your endpoint returned 422? We're hiring a Junior Backend Dev (Python + FastAPI + Pydantic v2 + SQLAlchemy). Real tickets, real mentorship, real code reviews. Show us in 30 mins, no LeetCode: [SkillSauce Assessment Link] #Python #FastAPI #DevJobs

LinkedIn Direct Message

Hi [Candidate Name], I came across your [FastAPI project / GitHub repo name] — nice work splitting your Pydantic schemas into create and read models and adding pytest coverage, which most portfolio projects skip. We're hiring a Junior Backend Developer at [Company Name] to build FastAPI endpoints on PostgreSQL, paired with a senior mentor from day one. Instead of a CV screen, we use a short 30-minute SkillSauce practical where you add a validated endpoint to a small FastAPI app: [SkillSauce Assessment Link] Would you be up for giving it a try, or for a quick 15-minute chat first? Best, [Your Name]

Show template

Email Sequence for Junior FastAPI Candidates

A short, encouraging sequence that tells junior candidates exactly what they will build, how they will be evaluated, and what support they will get.

Cold Sourcing

Subject: Junior FastAPI Developer role at [Company Name] — mentorship from day one

Show template

Hi [Candidate Name], I saw your work on [Project/Repository] and liked that you used Depends for the database session and kept your routers, schemas, and models in separate modules instead of one large main.py. At [Company Name] we're growing our Python API team and are hiring a Junior Backend Developer (FastAPI). You'd build typed REST endpoints on FastAPI, Pydantic v2, and PostgreSQL, write tests with pytest, and ship to staging within your first 90 days — with a senior engineer mentoring you throughout. Our process starts with a practical 30-minute SkillSauce exercise (add a validated endpoint to a small FastAPI app and make the tests pass) rather than algorithm puzzles: [SkillSauce Assessment Link] Would you be open to a short call this week? Best regards, [Your Name] [Your Title], [Company Name]

Interview Invite

Subject: Next steps: Junior Backend Developer (FastAPI) at [Company Name]

Show template

Hi [Candidate Name], Thanks for your interest in the Junior Backend Developer (FastAPI) role! Our next stage has two short parts you can complete on your own schedule within the next 4 business days: 1. SkillSauce Practical Assessment (30 mins): You'll receive a small FastAPI project with SQLAlchemy and SQLite. Add a create and a fetch endpoint with Pydantic validation, return the right status codes, and make the pytest + TestClient suite pass: [SkillSauce Assessment Link] 2. TESSA AI Interview (15 mins): A conversational interview about how you learn, how you debug a failing request, and how you work with code review feedback: [TESSA Interview Link] Tip: run pytest -q before submitting and feel free to use the auto-generated /docs page to try your endpoints. Once our team reviews your results, we'll schedule a friendly 45-minute pair-debugging session with one of our senior engineers. Good luck! [Your Name] [Company Name]

Offer Letter

Subject: Offer of Employment: Junior Backend Developer (FastAPI) at [Company Name]

Show template

Dear [Candidate Name], We're delighted to offer you the position of Junior Backend Developer (FastAPI) at [Company Name]! Your SkillSauce submission stood out for its clean Pydantic schemas and complete tests, and our engineers enjoyed tracking down the blocking time.sleep() call with you in the pairing session. Offer Summary: • Position: Junior Backend Developer (FastAPI) • Base Salary: [Salary Amount] per annum • Equity / Bonus: [Equity or Bonus Details] • Start Date: [Start Date] • Reporting To: [Engineering Manager Name] • Mentor: [Senior Engineer Name], Senior Python Engineer • Learning Budget: [Amount] per year for courses, books, and conference tickets • Equipment: Laptop with 32 GB RAM, PyCharm Professional or VS Code setup of your choice, and home-office stipend • Benefits: Health, dental, and vision coverage, [PTO Days] days paid time off, and retirement matching Please sign and return the attached agreement by [Offer Expiration Date]. We can't wait to see your first pull request merged! Warm regards, [Your Name] [Company Name]

Junior FastAPI SkillSauce & TESSA Assessment Blueprint

A two-part evaluation that confirms the candidate can build and test a real FastAPI endpoint, then measures the coachability and communication that drive junior ramp-up speed.

30 mins

SkillSauce Junior FastAPI CRUD & Validation Assessment

Candidates receive a FastAPI project with SQLAlchemy 2.0, SQLite, and a get_db yield dependency. They must implement POST /items (201 with the created resource) and GET /items/{item_id} (404 via HTTPException for unknown IDs), constrain input with Field(min_length=1, max_length=100) and Field(gt=0), declare a response_model that omits the internal cost_price column, and pass a hidden pytest suite. Graders also check that no Pydantic v1 APIs are used and that no blocking calls appear inside async def handlers.

Focus:: Path operations with APIRouter, Pydantic v2 request/response models, Depends session injection, HTTPException status codes, and pytest + TestClient.

15 mins

TESSA AI Learning Agility & Communication Interview

TESSA runs an adaptive conversational interview asking the candidate how they would investigate a frontend report of '422 Unprocessable Entity', how they would explain Depends to a teammate coming from Flask, how they incorporate reviewer feedback, and what they learned from their last Python project. Responses are scored for clarity, honesty about gaps, and structured troubleshooting. TESSA AI runs it as a Behavioural round and returns a scored report for the hiring team.

Focus:: Explaining dependency injection and validation, structured debugging, responding to code review, and asking for help.

Why This Assessment Mix

Junior FastAPI hires rarely fail on Python syntax; they fail on misunderstanding what the framework does for them: unregistered routers, blocking calls inside async def, Pydantic v1 idioms in a v2 codebase, ORM objects returned without a response model, and untested code paths. The 30-minute SkillSauce task exercises exactly these first-quarter tickets in a real FastAPI project with automated pytest grading, while TESSA measures the coachability and communication that determine how quickly a junior becomes independent. Together they predict ramp-up speed far better than resume keywords like 'Python' or 'REST API'.

You can run tests like these in SkillSauce: build an IT skills assessment from the test library and turn on AI proctoring for remote candidates.

Behavioral & Fundamentals Rubric for Junior FastAPI Developers

Use these questions in the live session to check framework understanding, debugging habits, and how the candidate learns from feedback.

Walk me through what happens when a JSON POST request hits a FastAPI endpoint, from Uvicorn receiving it to the response being sent.

Why it matters: Separates candidates who understand the ASGI server, routing, dependency resolution, and Pydantic validation from those who only memorized decorators.
What to listen for: Green flags: Uvicorn passing the ASGI request to the app, the router matching the path and method, dependencies being resolved, the body parsed and validated into the Pydantic model (422 on failure), the function running, and the return value filtered through response_model and serialized to JSON. Red flags: cannot explain where the 422 comes from or treats FastAPI as magic.

Tell me about a bug in one of your Python projects that took you more than a day to solve. How did you eventually find it?

Why it matters: Juniors will often be stuck; how they investigate and when they escalate determines team productivity.
What to listen for: Green flags: reading the full traceback from the bottom up, reproducing the bug in a failing pytest, using breakpoint() or the IDE debugger, logging the SQL emitted with echo=True on the engine, and asking for help with a clear summary of what was tried. Red flags: random code changes until it works, or never asking for help.

When would you write a path operation with `async def` and when with plain `def`?

Why it matters: The single most common production mistake in FastAPI codebases is blocking the event loop; juniors must know the rule before they touch shared services.
What to listen for: Green flags: use async def when every I/O call is awaited (httpx.AsyncClient, an async DB driver); use def when calling synchronous libraries such as requests or a sync SQLAlchemy session, because FastAPI runs it in a threadpool. Red flags: 'async is always faster', or calling time.sleep() inside async def.

Describe a time you received critical feedback on your code. What did you change?

Why it matters: Code review is the primary learning channel for juniors in a FastAPI codebase with established conventions for routers, schemas, and dependencies.
What to listen for: Green flags: a specific example (e.g., moving database logic out of the path operation into a service or repository module, or replacing a global session with a Depends dependency), understanding the reason, and applying it in later PRs. Red flags: defensiveness or vague answers.

TESSA, the SkillSauce AI interviewer, can run a structured first-round interview built around questions like these. See how TESSA AI interviews work.

Verified Junior FastAPI Technical Questions

Five EASY-tier multiple-choice questions taken verbatim from the SkillSauce FastAPI question bank (it/question_bank/FastAPI.json), covering route decorators, Pydantic validation, the ASGI server, background tasks, and global exception handling.

In FastAPI, which decorator is used to define a route that handles GET requests?

Easy

a@app.getCorrect
b@app.delete
c@app.put
d@app.post

Explanation: FastAPI uses the @app.get decorator to define endpoints that handle GET requests.

Why it matters: Confirms the candidate knows FastAPI's path operation decorators map one-to-one to HTTP methods. Follow up by asking how the same decorator is used on an APIRouter (@router.get) and how status_code=201 or response_model= are passed to it.

If you need to validate incoming JSON data in a FastAPI endpoint, which tool should you use?

Easy

aJSON Schema validation externally
bManual dictionary checks
cPydantic modelsCorrect
dSQLAlchemy models

Explanation: FastAPI leverages Pydantic for request data validation, making it simple and effective.

Why it matters: Checks that the candidate understands Pydantic models are FastAPI's validation and serialization layer, distinct from SQLAlchemy ORM models. Strong juniors mention Field constraints, the automatic 422 response, and that the same model appears in the generated OpenAPI schema.

When preparing to deploy a FastAPI application in production, which ASGI server is commonly recommended?

Easy

aDjango
bUvicornCorrect
cTornado
dFlask

Explanation: Uvicorn is a fast ASGI server frequently recommended for deploying FastAPI applications in production.

Why it matters: Verifies awareness that FastAPI is an ASGI application that needs an ASGI server to run. Follow up on the difference between uvicorn main:app --reload for development and running without --reload (and with --workers or fastapi run) in production.

How can you implement background tasks in FastAPI to perform operations after sending the response?

Easy

aUse the BackgroundTasks parameterCorrect
bUse a while loop in the endpoint
cStart a new thread manually
dSchedule tasks using Celery exclusively

Explanation: FastAPI provides a built-in BackgroundTasks parameter to easily run tasks after the response is returned.

Why it matters: Tests knowledge of the built-in BackgroundTasks parameter for small post-response work such as sending a welcome email or writing an audit row. Ask a follow-up: what happens to the task if the process restarts — a good junior realizes it runs in the same process and is not durable.

What is the simplest way to implement global exception handling in a FastAPI application?

Easy

aUse a global middleware to catch all exceptions
bRely on the default Python exception system
cRegister custom exception handlers using app.exception_handlerCorrect
dWrap every route with try-except blocks

Explanation: FastAPI allows you to register custom exception handlers globally using app.exception_handler, which is the recommended approach.

Why it matters: Assesses whether the candidate keeps error handling centralized: mapping a domain exception like ItemNotFoundError to a consistent JSON body with @app.exception_handler, instead of duplicating try/except in every path operation. Strong juniors also know HTTPException and RequestValidationError can be customized the same way.

Need more questions for this stack? Browse sample questions by skill in the SkillSauce Question Library.

Constructive Rejection Templates for Junior FastAPI Candidates

Junior candidates remember how they were treated. Give specific, learnable feedback and point them to resources so your employer brand grows in the Python community even when you say no.

Post-Assessment Rejection

Subject: Your application for Junior Backend Developer (FastAPI) at [Company Name]

Show template

Hi [Candidate Name], Thank you for taking the time to complete our SkillSauce FastAPI assessment. We know a hands-on exercise takes real effort, and we appreciate it. After reviewing your SkillSauce report, we've decided not to move forward at this stage. The hidden test suite showed gaps in a few areas that are central to this role — specifically [e.g., returning 404 with HTTPException for missing items / separating request and response Pydantic models / pytest coverage of error paths]. These are very learnable: the official FastAPI tutorial sections on 'Response Model' and 'Handling Errors' (fastapi.tiangolo.com) are a great next step. We'd genuinely welcome a new application in 6 months as you build more FastAPI experience. Best of luck with your search! Kind regards, [Your Name] [Company Name]

Post-Interview Rejection

Subject: Thank you for interviewing with [Company Name]

Show template

Hi [Candidate Name], Thank you for spending time with our team during the pair-debugging session. [Interviewer Name] particularly appreciated [specific positive, e.g., how quickly you spotted the router that was never included in the app]. After careful consideration, we've decided to proceed with another candidate whose experience more closely matches our current needs. This was a close decision. If helpful, our feedback is that strengthening your understanding of [e.g., when to use async def versus def, and how the event loop is blocked] would make you an even stronger candidate. We'd be glad to keep your profile on file for future junior openings and hope our paths cross again. Warm regards, [Your Name] [Company Name]

Mid-Level level

Mid-Level Backend Developer (FastAPI)

Experience: 3 - 6 Years
Average time to hire: 18 - 25 Days

Core tech stack

FastAPI (Depends, APIRouter, lifespan)
Pydantic v2 & pydantic-settings
SQLAlchemy 2.0 async + asyncpg
PostgreSQL + Alembic
OAuth2 / JWT (PyJWT, scopes)
Redis (redis.asyncio caching)
Celery / ARQ task queues
pytest + httpx AsyncClient, Docker

How to Hire a Mid-Level Backend Developer (FastAPI)

Mid-level FastAPI engineers (3–6 years) own features end to end in production. The bar moves from 'can build an endpoint' to 'can keep an async service fast, secure, and correct under real load': using SQLAlchemy 2.0 AsyncSession with asyncpg and a request-scoped yield dependency, avoiding MissingGreenlet lazy-load errors with selectinload, managing schema changes with Alembic, securing routes with OAuth2PasswordBearer, JWTs, and Security scopes, adding Redis caching, moving durable work off BackgroundTasks onto Celery or ARQ, and testing with httpx.AsyncClient and app.dependency_overrides. Focus the process on evidence of production ownership and on how the candidate reasons about the event loop.

1
Scope the Role Around Service Ownership
Define which FastAPI services the hire will own (e.g., billing or catalog), the on-call expectations, and the integration surface: PostgreSQL via async SQLAlchemy, Redis, a Celery or ARQ worker fleet, and third-party APIs called with httpx.AsyncClient. This filters out candidates who have only worked on isolated endpoints.
2
Source Engineers With Production FastAPI Footprints
Look for engineers who answer [fastapi] and [sqlalchemy] questions on Stack Overflow, participate in FastAPI GitHub Discussions, or contribute to ecosystem libraries such as SQLModel, fastapi-users, fastapi-pagination, or Alembic. Use LinkedIn boolean strings combining 'FastAPI' with 'asyncpg', 'Alembic', 'Celery', or 'Redis'. Share the job from your Career Page in these channels, and add people who have not applied from Candidate Lookup or by their LinkedIn profile URL.
3
Administer a 45-Minute SkillSauce Async Performance & Security Assessment
Provide an orders service with a slow endpoint: an async def handler that uses a synchronous SQLAlchemy session, plus lazy-loaded relationships causing N+1 queries. Candidates must move to AsyncSession with selectinload, secure write routes with OAuth2 bearer JWTs and scopes, accept filters through a Pydantic query-parameter model, and add httpx.AsyncClient tests using dependency_overrides. Pick the questions from the Question Library, or let TESSA AI generate them when your exact skill mix is missing, and keep AI proctoring on for remote candidates.
4
Use a 20-Minute TESSA AI Interview for Ownership & Trade-Offs
TESSA probes how the candidate handled a production incident (event-loop stalls, connection pool exhaustion with QueuePool limit ... overflow ... reached, a bad Alembic migration), how they negotiate breaking OpenAPI contract changes with frontend and mobile teams, and how they balance delivery pressure against technical debt. TESSA runs this AI interview in any of 100+ languages and returns a scored report you can share with the team.
5
Run a 60-Minute Live Code Review & Design Session
Ask the candidate to review a pull request containing a module-level AsyncSession shared across requests, a BackgroundTasks job that reuses the request's database session, a missing await on a coroutine, and a response that returns an ORM object without a response_model. Then have them design an idempotent payment-webhook endpoint backed by an Idempotency-Key header, a unique constraint, and a queued job.
6
Close With a Competitive, Ownership-Focused Offer
Benchmark against regional Python market data — FastAPI engineers are heavily contested by AI/ML product teams that need model-serving APIs — highlight the services they will own, and aim to close within 18–25 days.

Pro Tips

Ask why an async def endpoint that uses a synchronous SQLAlchemy Session makes the whole service slow under load. Strong candidates explain that each query blocks the event loop, and offer two fixes: switch to AsyncSession with asyncpg, or declare the endpoint with def so FastAPI runs it in the AnyIO threadpool (and know that pool is bounded, 40 threads by default).
Probe async SQLAlchemy specifics: why accessing a lazy relationship raises MissingGreenlet, how selectinload/joinedload or lazy='raise' prevent hidden I/O, and why async_sessionmaker(expire_on_commit=False) is the usual setting for request-scoped sessions.
Check resource lifecycle: the engine, Redis client, and a shared httpx.AsyncClient should be created once in a lifespan async context manager (the replacement for deprecated @app.on_event('startup')), not per request. Creating create_async_engine() inside a dependency is a classic pool-exhaustion bug.
Verify they know the limits of BackgroundTasks: work runs in the same process after the response, is lost on a crash or deploy, and should not reuse the request's database session. Anything that must not be lost — emails, payments, webhooks — belongs on Celery, ARQ, or another durable queue.

Mid-Level Backend Developer (FastAPI) Job Description Template

Use this template to attract experienced FastAPI engineers who want end-to-end ownership of a production async service rather than a queue of isolated tickets.

Mid-Level Backend Developer (FastAPI) Job Description

Role Overview

We're looking for a Mid-Level Backend Developer (FastAPI) to own core services in our Python 3.12 / FastAPI platform. You'll design and ship features end to end — typed REST APIs, async persistence with SQLAlchemy 2.0, caching, background processing, and security — and you'll be accountable for how those services behave in production: latency, error rates, and data integrity.

First 90 Days Deliverables

Days 1–30: Run the [Service Name] service locally with Docker Compose (PostgreSQL, Redis, worker), join the on-call shadow rotation, and ship a performance fix for one of the top-3 slow endpoints in Grafana (target: p95 latency down 30% by replacing lazy-loaded relationships with selectinload, removing a blocking call from an async def handler, and adding a missing composite index through an Alembic migration).
Days 31–60: Deliver a new feature slice end to end: a versioned APIRouter with documented OpenAPI responses, OAuth2 bearer JWT protection with Security(get_current_user, scopes=['orders:write']), a Redis-backed read cache with explicit invalidation on writes, and a pytest suite using httpx.AsyncClient with ASGITransport and a real PostgreSQL container in CI.
Days 61–90: Move a fragile BackgroundTasks email/webhook flow onto ARQ or Celery with retries, exponential backoff, and idempotency keys; add Prometheus metrics and OpenTelemetry spans for the feature; and present the design in an architecture review.

Must-Have Requirements

3–6 years of professional Python development, including at least 2 years building production FastAPI (or Starlette) services.
Deep understanding of async Python: the event loop, await semantics, what blocks it, and when to use the threadpool.
Production experience with SQLAlchemy 2.0 (async sessions, relationship loading strategies, transactions) and Alembic migrations on PostgreSQL.
Pydantic v2 expertise: validators, computed fields, model_config, discriminated unions, and pydantic-settings for configuration.
Experience securing APIs with OAuth2 flows, JWTs, scopes, and password hashing (bcrypt or Argon2).
Testing discipline: pytest, httpx.AsyncClient, app.dependency_overrides, and factory-based test data.
Docker and CI/CD experience (GitHub Actions, GitLab CI).

Nice-to-Have Skills

Message brokers and task queues: RabbitMQ, Redis Streams, Celery, ARQ, or Dramatiq.
WebSockets or Server-Sent Events for real-time features.
Observability with OpenTelemetry, Prometheus, Sentry, and structured logging (structlog).
Serving ML models or LLM-backed features behind FastAPI with streaming responses.
AWS (ECS/EKS, RDS, ElastiCache, SQS) or GCP equivalents (Cloud Run, Cloud SQL).

Benefits & Growth

Competitive mid-level salary plus equity or annual bonus.
Ownership of a core service and a clear path to Senior / Tech Lead.
Annual conference budget (PyCon, EuroPython) and course subscriptions.
Flexible hybrid/remote work, comprehensive health coverage, and paid on-call compensation.

Need a version for another role or tone? Draft one with the free Job Description Generator.

Targeted Sourcing Channels for Mid-Level FastAPI Engineers

Experienced FastAPI engineers leave public traces of production work: async debugging answers, issue threads in the framework's discussions, and contributions to the SQLAlchemy ecosystem. Source where that evidence lives.

SkillSauce

Candidate Lookup

Search by domain and seniority, then narrow by the companies and locations you want to hire from. The people you pick join the same pipeline as your applicants.

SkillSauce

Career Page and LinkedIn Import

Publish the job on your Career Page and share the link on LinkedIn, X and job boards: every applicant lands in the role’s Job Profile. Import a LinkedIn profile by its URL when you spot someone.

Social network

X (Twitter) (opens in a new tab)

x.com

Post the role with hashtags such as #Python and #FastAPI, and join the X Communities where people in the field share their work.

Technical Forum

Stack Overflow [fastapi] Tag Contributors (opens in a new tab)

stackoverflow.com

Identify engineers with high-quality answers on dependency injection, async SQLAlchemy sessions, Pydantic v2 migration, and OAuth2 flows. Answer history is a public record of production debugging depth.

Developer Community

FastAPI GitHub Discussions (opens in a new tab)

github.com

The framework's official Q&A forum. Engineers who give clear, reproducible answers about lifespan events, middleware ordering, or testing with dependency overrides are typically running FastAPI in production and are strong mid-level candidates.

Open Source Directory

GitHub SQLAlchemy & Alembic Contributors (opens in a new tab)

github.com

Contributors and thoughtful issue reporters on SQLAlchemy and Alembic understand async sessions, loading strategies, and migration pitfalls — the exact data-layer skills a mid-level FastAPI owner needs.

Job Board

Hacker News 'Who is Hiring?' Threads (opens in a new tab)

news.ycombinator.com

Monthly threads read by experienced engineers. Post a concise listing that names FastAPI, async SQLAlchemy, and the production service they will own, plus your practical-only interview process.

Recruitment Platform

LinkedIn Boolean Search – Production FastAPI Engineers (opens in a new tab)

linkedin.com

Use: ('FastAPI' AND 'Python') AND ('asyncpg' OR 'SQLAlchemy' OR 'Alembic') AND ('Celery' OR 'Redis' OR 'RabbitMQ') AND ('Docker' OR 'Kubernetes') NOT ('Intern' OR 'Trainee'). Filter for 3–6 years and titles like Backend Engineer or Software Engineer II.

Job boards

Job Boards

Post the job on Indeed and Glassdoor, and on Naukri and foundit for hiring in India. Where a board allows it, use your Career Page link as the apply link, so applicants land in the same Job Profile.

Referrals

Employee Referrals

Ask your team who they have worked with. Referred candidates take the same assessment and interview as everyone else.

Mid-level engineers respond to outreach that speaks their language — real async production problems, real ownership, and a respectful, practical process.

LinkedIn Post

Hiring: Mid-Level Backend Developer (FastAPI) You've shipped FastAPI services to production. You've hunted a time.sleep() hiding in an async def, fixed a MissingGreenlet with selectinload, and moved a flaky BackgroundTasks job onto a real queue. Come own a core service with us. Stack: Python 3.12, FastAPI, Pydantic v2, SQLAlchemy 2.0 async + asyncpg, PostgreSQL + Alembic, Redis, ARQ/Celery, Docker, AWS You'll own: [Service Domain] — APIs, data model, performance, and on-call Growth: clear path to Senior / Tech Lead, conference budget Skip the trivia rounds — start with a 45-minute practical SkillSauce challenge: [SkillSauce Assessment Link] #FastAPI #Python #AsyncIO #SQLAlchemy #BackendEngineering #Hiring

Show template

Hiring: Mid-Level Backend Developer (FastAPI) You've shipped FastAPI services to production. You've hunted a time.sleep() hiding in an async def, fixed a MissingGreenlet with selectinload, and moved a flaky BackgroundTasks job onto a real queue. Come own a core service with us. Stack: Python 3.12, FastAPI, Pydantic v2, SQLAlchemy 2.0 async + asyncpg, PostgreSQL + Alembic, Redis, ARQ/Celery, Docker, AWS You'll own: [Service Domain] — APIs, data model, performance, and on-call Growth: clear path to Senior / Tech Lead, conference budget Skip the trivia rounds — start with a 45-minute practical SkillSauce challenge: [SkillSauce Assessment Link] #FastAPI #Python #AsyncIO #SQLAlchemy #BackendEngineering #Hiring

Twitter / X Post

Show template

Know why one blocking call in an async def slows down every request on the worker? We're hiring a Mid-Level FastAPI Engineer (Pydantic v2, async SQLAlchemy, Redis, ARQ) to own a core service end to end. 45-min practical test, no whiteboards: [SkillSauce Assessment Link] #Python #FastAPI #BackendJobs

LinkedIn Direct Message

Hi [Candidate Name], I noticed your experience at [Current Company] running FastAPI services with [async SQLAlchemy / Celery / Redis] — and your [GitHub discussion answer / blog post / talk] on [topic] was genuinely useful. At [Company Name] we're looking for a Mid-Level Backend Engineer to own our [Service Domain] service: FastAPI with Pydantic v2, async PostgreSQL, a queue-backed worker fleet, and real production ownership with a fair on-call rotation. Our process is short and practical — a 45-minute SkillSauce exercise based on a real slow async endpoint we fixed last year: [SkillSauce Assessment Link]. Open to a 20-minute chat to hear more? Best, [Your Name]

Show template

Email Sequence for Mid-Level FastAPI Candidates

Lead with the concrete service and problems the candidate will own, explain the two-part evaluation clearly, and make the offer about scope as much as salary.

Cold Sourcing

Subject: Own our [Service Domain] service — FastAPI Engineer at [Company Name]

Show template

Hi [Candidate Name], Your work on [Project/Company] caught my eye — particularly [specific detail, e.g., migrating a Flask service to FastAPI with async SQLAlchemy and Alembic without downtime]. At [Company Name], our [Service Domain] service handles [X] requests per day on FastAPI, PostgreSQL, and Redis, with an ARQ worker fleet for background jobs. We're hiring a Mid-Level Backend Developer to own it: keep the event loop healthy, tune async queries, harden OAuth2/JWT security, and make background processing reliable. Our process respects your time: a 45-minute SkillSauce practical based on a real performance issue from our codebase, a 20-minute TESSA AI conversation, and a single live design session. Preview the challenge here: [SkillSauce Assessment Link] Would you be open to a 15-minute call this week? Best regards, [Your Name] [Your Title], [Company Name]

Interview Invite

Subject: Technical evaluation: Mid-Level Backend Developer (FastAPI) at [Company Name]

Show template

Hi [Candidate Name], Thanks for a great conversation! We'd love to move you to our technical evaluation, which has two parts you can complete within the next 3 business days: 1. SkillSauce Practical Assessment (45 mins): You'll work in a FastAPI orders service with a slow endpoint. Move it to an async SQLAlchemy session without N+1 queries, protect write routes with OAuth2 bearer JWTs and scopes, accept filters through a Pydantic query model, and add httpx AsyncClient tests with dependency overrides: [SkillSauce Assessment Link] 2. TESSA AI Interview (20 mins): A conversational interview on production incidents you've handled, how you evolve API contracts, and how you balance delivery with technical debt: [TESSA Interview Link] After we review your results, we'll schedule a 60-minute live session with [Interviewer Name], our Staff Engineer, covering a code review and a small design exercise. Best regards, [Your Name] [Company Name]

Offer Letter

Subject: Formal Offer: Backend Developer (FastAPI) at [Company Name]

Show template

Dear [Candidate Name], On behalf of [Company Name], I'm excited to offer you the position of Backend Developer (FastAPI)! The team was impressed by how quickly you spotted the blocking session inside the async endpoint in your SkillSauce assessment and by your idempotency-key design for the payment webhook in the live session. Offer Summary: • Position: Mid-Level Backend Developer (FastAPI) • Base Salary: [Salary Amount] per annum • Equity / Bonus: [Equity Grant or Bonus Structure] • Start Date: [Start Date] • Reporting To: [Engineering Manager Name] • Service Ownership: [Service Domain] service, with a 90-day onboarding plan and a named onboarding buddy • On-Call: [Rotation Frequency] rotation with [On-Call Compensation] • Learning: [Amount] annual budget for conferences (PyCon, EuroPython) and courses • Equipment: Laptop or workstation of choice, PyCharm Professional license, home-office stipend • Benefits: Comprehensive health coverage, [PTO Days] days PTO, retirement matching Please review the attached agreement and return it by [Offer Expiration Date]. We're looking forward to building with you! Warm regards, [Your Name] [Company Name]

Mid-Level FastAPI SkillSauce & TESSA Assessment Blueprint

An evaluation that reproduces the failure modes mid-level FastAPI engineers must handle in production — event-loop blocking, async ORM misuse, and inconsistent security — and grades them objectively.

45 mins

SkillSauce Mid-Level FastAPI Async Performance, Security & Testing Assessment

Candidates inherit a FastAPI orders service where GET /customers/{customer_id}/orders is an async def handler using a synchronous session and emits 101 SQL statements for 100 orders. They must switch to an async session dependency with asyncpg, load line items with selectinload to bound the query count, require orders:write scope on mutating routes via OAuth2PasswordBearer and decoded JWT claims, accept status, created_after, and limit filters through a Pydantic model declared as query parameters, and add tests that override the auth and database dependencies. Automated grading counts SQL statements, measures concurrent request latency to detect event-loop blocking, and runs a hidden security test suite.

Focus:: Event-loop hygiene, SQLAlchemy 2.0 AsyncSession with selectinload, request-scoped yield dependencies, OAuth2 bearer JWTs with Security scopes, Pydantic query-parameter models, and httpx.AsyncClient tests with dependency_overrides.

20 mins

TESSA AI Production Ownership & Trade-Off Interview

TESSA asks the candidate to walk through a real incident they owned (e.g., an event-loop stall caused by a sync SDK, a SQLAlchemy pool timeout, or an Alembic migration that locked a hot table), how they introduced a breaking change to an OpenAPI contract consumed by mobile clients, and how they decide between shipping and refactoring. Scoring emphasizes structured root-cause reasoning, ownership language, and clear communication of trade-offs. TESSA AI runs it as an AI interview and returns a scored report for the hiring team.

Focus:: Incident response, API contract evolution, technical debt prioritization, and cross-team collaboration.

Why This Assessment Mix

Mid-level FastAPI engineers are hired to own services, and the most expensive production failures in FastAPI codebases come from blocking the event loop, misusing async SQLAlchemy sessions, treating BackgroundTasks as a durable queue, and inconsistent authorization. The SkillSauce task reproduces these failure modes in a real service and grades them objectively (SQL counts, concurrency latency, security tests), while TESSA verifies the ownership mindset and communication needed to run a service in production. The combination reliably separates engineers who have operated async Python under load from those who have only built tutorial CRUD apps.

You can run tests like these in SkillSauce: build an IT skills assessment from the test library and turn on AI proctoring for remote candidates.

Ownership & Collaboration Rubric for Mid-Level FastAPI Engineers

These questions assess production ownership, API stewardship, risk communication, and the code review habits that make a mid-level engineer a force multiplier.

Tell me about the most serious production incident you handled in a FastAPI service. How did you find the root cause, and what did you change afterwards?

Why it matters: Mid-level engineers are expected to own services operationally, not just write endpoints.
What to listen for: Green flags: concrete signals (p99 latency, event-loop lag, SQLAlchemy pool checkout timeouts, worker restarts), tools such as py-spy, Sentry traces, or OpenTelemetry spans, a quick mitigation followed by a durable fix (e.g., replacing a sync SDK with an async client or moving it to the threadpool), and a blameless post-mortem with action items. Red flags: blaming others, no data, or a 'fix' that only added more Uvicorn workers.

You need to rename a field in a public API that the web and mobile apps both consume. How do you roll it out?

Why it matters: Tests API contract discipline; FastAPI's generated OpenAPI schema is often used to generate clients, so careless changes break consumers immediately.
What to listen for: Green flags: additive change first (Pydantic Field(alias=...) or validation_alias/serialization_alias to accept both names), marking the old field deprecated=True in the schema, diffing the OpenAPI document in CI, coordinating mobile release cycles, and removing the old field only after usage metrics drop. Red flags: changing it and telling teams afterwards.

Describe a time you pushed back on a feature deadline because of a technical risk. What happened?

Why it matters: Reveals judgment and the ability to communicate risk to product stakeholders.
What to listen for: Green flags: quantified risk (lost background jobs on deploy, a migration that would lock a large table, missing authorization checks), proposed alternatives (feature flag, phased rollout, an expand-and-contract migration), and alignment reached with product. Red flags: silently cutting corners, or refusing without offering options.

How do you review a pull request from a junior engineer that works but has design problems?

Why it matters: Mid-level engineers multiply the team through reviews.
What to listen for: Green flags: separating blocking issues (a sync HTTP call inside async def, business logic living in the path operation, a missing response_model leaking fields) from preferences, explaining the why with links to team conventions, and offering to pair. Red flags: rewriting the code themselves or nitpicking formatting that Ruff should enforce.

TESSA, the SkillSauce AI interviewer, can run a structured first-round interview built around questions like these. See how TESSA AI interviews work.

Verified Mid-Level FastAPI Technical Questions

Five MEDIUM-tier multiple-choice questions taken verbatim from the SkillSauce FastAPI question bank (it/question_bank/FastAPI.json), covering concurrency and workers, Pydantic query models, non-blocking database access, centralized error handling, and WebSocket connection management.

In a high-traffic production environment, which approach would be most effective for handling concurrent requests in a FastAPI application?

Medium

aUse blocking I/O operations with manual thread management
bRely solely on synchronous endpoints with a single Uvicorn worker
cUse asynchronous endpoints with async def and run Uvicorn with multiple workersCorrect
dImplement caching without asynchronous programming

Explanation: Using asynchronous endpoints along with deploying Uvicorn with multiple workers ensures efficient handling of concurrent requests and better scalability in production environments.

Why it matters: Evaluates the candidate's concurrency model: async def gives concurrency within one process only if every I/O call is awaited, and multiple Uvicorn worker processes add CPU parallelism past the GIL. Push further: what happens if one handler calls a blocking SDK, and how many workers per CPU core make sense.

When validating and parsing complex query parameters in a FastAPI project, which approach provides the most robust real-world solution?

Medium

aPerform manual string parsing inside endpoint functions
bRely on default type coercion of Python without validation
cUse Python's built-in JSON module for query parsing
dIntegrate Pydantic models directly into query parametersCorrect

Explanation: Integrating Pydantic models directly into query parameters leverages FastAPI's strong validation features, ensuring that the data is correctly parsed and validated, which is essential in real-world applications.

Why it matters: Tests whether the candidate keeps filter and pagination logic declarative. Strong answers mention declaring a Pydantic model with Annotated[FilterParams, Query()] (supported in recent FastAPI versions), model_config = {'extra': 'forbid'} to reject unknown parameters, and Field(le=100) limits that also show up in the OpenAPI docs.

Your FastAPI microservice needs to interact with a SQL database without blocking asynchronous endpoints. Which approach is best for achieving non-blocking database operations?

Medium

aRely on raw synchronous connections without proper concurrency handling
bUse synchronous SQLAlchemy ORM operations directly in async endpoints
cUse an asynchronous database driver like asyncpgCorrect
dExecute blocking queries in separate thread pools in every endpoint

Explanation: Employing an asynchronous database driver like asyncpg ensures that database operations do not block the asynchronous event loop in FastAPI, making it ideal for real-world, high-performance applications.

Why it matters: Probes the async data layer: SQLAlchemy 2.0 create_async_engine('postgresql+asyncpg://...'), async_sessionmaker, and a yield dependency per request. Good candidates also flag the async pitfalls — MissingGreenlet on lazy loads, needing eager loading, and never sharing one AsyncSession across concurrent tasks.

In a production FastAPI application, what is the most effective method to implement robust error handling and logging when an unexpected exception occurs?

Medium

aRely solely on the default HTTPException responses provided by FastAPI
bUse individual try-except blocks in every endpoint without central management
cCreate custom exception handlers using FastAPI's exception_handler decoratorCorrect
dAllow stack traces to be returned to the client for easier debugging

Explanation: Using custom exception handlers takes advantage of FastAPI's built-in capabilities to centralize error handling and logging, which is crucial for maintaining robust, production-ready applications.

Why it matters: Checks production error-handling design: a handler for domain exceptions, a catch-all handler for Exception that logs with a request/trace ID and returns a generic 500 without leaking internals, and customized RequestValidationError output. Ask how they would ensure the error also reaches Sentry or OpenTelemetry.

When adding real-time updates to a FastAPI application via WebSockets, what is the most effective strategy for managing multiple client connections?

Medium

aUse FastAPI's WebSocket endpoints combined with an in-memory connection managerCorrect
bImplement server-sent events (SSE) instead of real WebSocket connections
cUse external libraries without utilizing FastAPI's native WebSocket support
dRely on REST endpoints with frequent client polling

Explanation: FastAPI's native WebSocket support, along with an in-memory connection manager, provides an efficient solution for handling persistent connections and broadcasting messages to multiple clients in real-time.

Why it matters: Assesses real-time feature design with @app.websocket and a ConnectionManager that tracks sockets and handles WebSocketDisconnect. Follow up: an in-memory manager only knows the clients on its own worker process, so with multiple workers or pods a Redis pub/sub or broadcast layer is needed to fan out messages.

Need more questions for this stack? Browse sample questions by skill in the SkillSauce Question Library.

Constructive Rejection Templates for Mid-Level FastAPI Candidates

Experienced engineers talk to each other. Specific, respectful feedback protects your reputation in the Python community and keeps strong near-miss candidates warm.

Post-Assessment Rejection

Subject: Update on your Backend Developer (FastAPI) application at [Company Name]

Show template

Hi [Candidate Name], Thank you for completing the SkillSauce assessment for our Mid-Level Backend Developer (FastAPI) role — we appreciate the time and focus it takes. After reviewing your SkillSauce report, we won't be moving forward at this time. The areas where your submission fell short of what this role needs on day one were [e.g., removing the blocking database call from the async endpoint / bounding the query count with eager loading / enforcing JWT scopes on write routes]. Because this role involves immediate ownership of a production service, we have to weigh these heavily. This feedback reflects a single timed exercise, not your overall ability. We'd be happy to consider you for future openings and wish you the best in your search. Kind regards, [Your Name] [Company Name]

Post-Interview Rejection

Subject: Thank you for interviewing with [Company Name]

Show template

Hi [Candidate Name], Thank you for the time you invested in our interview process, including the live design session with [Interviewer Name]. The team especially valued [specific strength, e.g., your clear explanation of why the webhook needed an idempotency key and a unique constraint]. After a lot of deliberation, we've decided to move forward with another candidate whose production experience aligns more closely with [specific need, e.g., our queue-heavy background processing roadmap]. If it's useful, our interviewers felt that deeper hands-on experience with [e.g., async SQLAlchemy loading strategies / Celery or ARQ retry semantics] would strengthen your profile for similar roles. We'd like to stay in touch — I'll reach out if a role opens that's a stronger match. Thank you again, and best of luck. Warm regards, [Your Name] [Company Name]

Senior level

Senior Backend Developer (FastAPI)

Experience: 6+ Years
Average time to hire: 25 - 40 Days

Core tech stack

FastAPI & Starlette ASGI internals
Python asyncio / AnyIO & uvloop
Uvicorn / Gunicorn process model
SQLAlchemy 2.0 async + PgBouncer
Celery / ARQ + RabbitMQ or Kafka (transactional outbox)
Redis (caching, pub/sub, rate limiting)
OAuth2 / OIDC (Keycloak, Auth0) & JWT
OpenTelemetry, Prometheus & Grafana
Kubernetes (probes, HPA, graceful shutdown)
AWS (EKS/ECS, RDS, ElastiCache, SQS)

How to Hire a Senior Backend Developer (FastAPI)

Senior FastAPI engineers (6+ years) set the technical direction for a fleet of async Python services. Beyond deep framework expertise — Starlette's ASGI middleware stack, dependency resolution and caching, lifespan state, Pydantic v2 performance — they make architecture decisions that hold up under scale and failure: the process model (Gunicorn with Uvicorn workers on VMs versus one Uvicorn process per pod on Kubernetes), PostgreSQL connection budgeting across workers and pods with PgBouncer, durable asynchronous work with Celery or ARQ and the transactional outbox, idempotent webhooks, OIDC integration with cached JWKS, WebSocket fan-out across pods, and end-to-end observability with OpenTelemetry. Evaluate for architectural judgment, operational depth, and the ability to raise the engineering bar across teams.

1
Align on Architectural Mandate and Scope
Decide whether you are hiring a senior IC to lead a domain, a tech lead for a squad, or a platform engineer who owns the internal FastAPI service template and shared libraries. Document the decisions they will drive: a Flask or Django REST Framework to FastAPI migration, a Pydantic v1 → v2 upgrade across services, a sync → async data-layer rewrite, or decomposing a monolith into event-driven services.
2
Source Proven Python Architects
Target PyCon and EuroPython speakers, guests on Python podcasts such as Talk Python To Me, and contributors to FastAPI, Starlette, Pydantic, SQLAlchemy, or Uvicorn. Long-form writing on async Python performance or large Pydantic migrations is a strong signal; referrals from former colleagues carry unusual weight at this level. Senior people rarely apply, so search Candidate Lookup by domain, seniority, company and location and reach out first with their available contact details.
3
Administer a 60-Minute SkillSauce Distributed Systems Assessment
Present a three-service FastAPI system (orders, payments, notifications) on Docker Compose where confirmation emails sent with BackgroundTasks disappear during deploys, duplicate payment webhooks double-charge, a per-request create_async_engine() exhausts PostgreSQL connections under load, and WebSocket clients drop behind Nginx. Candidates implement an outbox table with a Celery or ARQ relay, idempotent webhook handling, a shared engine created in lifespan with a deliberate pool budget, and correct proxy configuration. Set it up in IT Assessments with a coding test in any of 60+ languages, and keep AI proctoring on so a longer remote session stays fair.
4
Deploy a 25-Minute TESSA AI Architecture & Leadership Interview
TESSA explores how the candidate led a major migration, how they resolve architectural disagreements between teams (e.g., sync versus async data access, monorepo service template versus per-team freedom), and how they mentor mid-level engineers. Responses are scored for strategic thinking and influence without authority. TESSA runs this AI interview in any of 100+ languages, and a Behavioural round can follow to probe how they lead.
5
Conduct a 90-Minute System Design Deep Dive
Have the candidate design a real-time notifications or pricing platform on FastAPI: service boundaries, data ownership, the worker process model, connection budgeting (pods × workers × pool_size + max_overflow against max_connections), WebSocket fan-out with Redis pub/sub, rate limiting, caching tiers, and SLO-driven observability. Push on failure scenarios such as a broker outage, a slow downstream API without timeouts, or a JWKS endpoint that is unreachable.
6
Hold a Leadership & Cross-Functional Panel
Include an engineering manager and a product partner to evaluate how the candidate writes ADRs, communicates trade-offs to non-engineers, and sets standards such as a cookiecutter/Copier FastAPI service template with pre-wired auth, logging, tracing, health checks, and OpenAPI linting.
7
Close With a Senior-Calibrated Offer
Senior async Python architects are heavily recruited by AI platform companies, fintech, and big tech. Present scope, autonomy, and compensation (including equity) clearly, and target a decision within 25–40 days.

Pro Tips

Ask them to budget PostgreSQL connections for 12 pods running 4 Uvicorn workers each with SQLAlchemy pool_size=10, max_overflow=10. Strong answers compute the worst case (960 connections) against max_connections, introduce PgBouncer in transaction mode, and know that asyncpg's prepared-statement cache must be disabled (prepared_statement_cache_size=0 / statement_cache_size=0) behind it.
Probe event-loop health at scale: detecting blocking calls with asyncio debug mode and slow_callback_duration, sampling stuck workers with py-spy dump, offloading CPU-bound work (PDF rendering, model inference) to a process pool or worker queue, and tuning the AnyIO threadpool limiter for sync dependencies instead of blindly adding workers.
Discuss the process model: on Kubernetes, FastAPI's own deployment docs favour one Uvicorn process per container scaled with replicas and HPA, while Gunicorn with Uvicorn workers suits VMs. Look for graceful shutdown awareness — SIGTERM handling, --timeout-graceful-shutdown, a preStop sleep, and draining in-flight WebSockets.
Ask how they would add request-ID and trace propagation to 30 services. Strong candidates prefer pure ASGI middleware or OpenTelemetry's FastAPI instrumentation over BaseHTTPMiddleware (which adds overhead and has historically complicated streaming responses and contextvars), packaged in a shared internal library.
Check Pydantic v2 migration leadership: using bump-pydantic, the pydantic.v1 compatibility namespace for incremental moves, replacing json.loads + model_validate with model_validate_json for faster parsing, and benchmarking response serialization before and after.

Senior Backend Developer (FastAPI) Job Description Template

Use this template to attract senior Python engineers and architects who want to set direction for an async platform, not just deliver tickets within one.

Senior Backend Developer (FastAPI) Job Description

Role Overview

We are hiring a Senior Backend Developer (FastAPI) to lead the architecture of our Python platform — [N] FastAPI services running on Kubernetes and handling [X] requests per day, including real-time WebSocket traffic and queue-driven background processing. You will set technical direction, own the hardest reliability and performance problems, and raise the bar for every engineer through design reviews, mentorship, and shared platform tooling.

First 90 Days Deliverables

Days 1–30: Audit the service landscape — dependency graph, FastAPI/Pydantic/SQLAlchemy versions, Uvicorn worker and pool settings, PostgreSQL connection usage versus max_connections, event-loop lag and SLO dashboards, and incident history. Publish an Architecture Decision Record (ADR) summarizing the top five risks and a sequenced remediation plan.
Days 31–60: Lead the design and first implementation of a transactional outbox with an ARQ or Celery relay for the order and payment flows, including idempotent webhook consumers keyed on provider event IDs, retry and dead-letter policies, and httpx timeouts with circuit breaking around the payment provider. Pair with mid-level engineers to land it in two services.
Days 61–90: Ship an internal FastAPI service template and shared library that standardizes lifespan-managed clients, OIDC JWT validation with cached JWKS, OpenTelemetry tracing, structured JSON logging, liveness/readiness endpoints, and graceful shutdown; run a load test comparing the current Gunicorn setup with one-Uvicorn-per-pod plus HPA and publish the results.

Must-Have Requirements

6+ years of backend engineering, with deep production expertise in Python and FastAPI/Starlette internals (ASGI lifecycle, middleware, dependency injection, lifespan state).
Mastery of asyncio and AnyIO: cancellation, timeouts, task groups, backpressure, and diagnosing event-loop blocking.
Proven experience designing distributed systems: service decomposition, transactional outbox, idempotency, retries, and eventual consistency with Celery/ARQ and RabbitMQ, SQS, or Kafka.
Advanced PostgreSQL and SQLAlchemy 2.0 performance tuning: query plans, locking, async connection pooling, PgBouncer, and zero-downtime Alembic migrations.
Operating Python services on Kubernetes: probes, resource limits, autoscaling, graceful shutdown, and container image hardening.
A track record of mentoring engineers and leading cross-team technical initiatives through ADRs and design reviews.

Nice-to-Have Skills

Large-scale migrations: Flask/DRF → FastAPI, Pydantic v1 → v2, sync → async SQLAlchemy.
Real-time systems with WebSockets or SSE and Redis pub/sub fan-out.
Serving ML or LLM workloads behind FastAPI (streaming responses, GPU worker queues).
Cloud certification (AWS, GCP, or Azure) and infrastructure as code (Terraform).

Benefits & Impact

Senior/Staff-level compensation with meaningful equity.
Direct influence over platform architecture and engineering standards.
Generous conference and speaking budget (PyCon US, EuroPython, KubeCon).
Flexible remote work, comprehensive health benefits, and sabbatical eligibility.

Need a version for another role or tone? Draft one with the free Job Description Generator.

Sourcing Channels for Senior FastAPI Engineers & Python Architects

Senior async Python talent is rarely on job boards. Find them through conference rosters, open-source commit history in the ASGI and Pydantic ecosystem, and long-form technical conversations, then reach out with talk- or contribution-specific context.

SkillSauce

Candidate Lookup

Senior people rarely apply. Search by domain and seniority, narrow by company and location, and reach out first with their available contact details.

SkillSauce

Career Page and LinkedIn Import

Publish the job on your Career Page and share the link on LinkedIn, X and job boards: every applicant lands in the role’s Job Profile. Import a LinkedIn profile by its URL when you spot someone.

Social network

X (Twitter) (opens in a new tab)

x.com

Post the role with hashtags such as #Python and #FastAPI, and join the X Communities where people in the field share their work.

Industry Conference

PyCon US Speakers & Tutorial Instructors (opens in a new tab)

us.pycon.org

The largest annual Python conference. Speaker and tutorial rosters list practitioners leading async migrations, API platform work, and Python performance efforts at scale — ideal for warm, talk-specific outreach.

Industry Conference

EuroPython Conference Community (opens in a new tab)

europython-society.org

Europe's largest Python conference, run by the EuroPython Society. Past talk archives on FastAPI, asyncio internals, and Pydantic surface senior engineers who can articulate architectural trade-offs publicly.

Open Source Directory

GitHub Pydantic & Starlette Contributors (opens in a new tab)

github.com

External contributors to Pydantic, Starlette, and Uvicorn understand validation performance, ASGI middleware, and server internals at a depth few engineers reach. Review contributor graphs and well-reasoned issue threads to find practitioners working in industry.

Industry Publication

Talk Python To Me Podcast (opens in a new tab)

talkpython.fm

Long-running Python podcast whose guests include framework authors and engineers running large Python platforms. Guest lists are a curated directory of senior practitioners worth approaching with episode-specific context.

Recruitment Platform

LinkedIn Recruiter – Senior FastAPI Architect Search (opens in a new tab)

linkedin.com

Use: ('FastAPI' OR 'Starlette' OR 'asyncio') AND ('Architect' OR 'Staff' OR 'Principal' OR 'Tech Lead') AND ('Kubernetes' OR 'EKS' OR 'GKE') AND ('Celery' OR 'Kafka' OR 'RabbitMQ') AND ('PostgreSQL'). Prioritize profiles listing migrations, ADRs, or conference talks.

Developer community

Stack Overflow (opens in a new tab)

stackoverflow.com

Find people who answer questions in the tags your stack uses: their answers show how they reason and explain.

Job boards

Job Boards

Post the job on Indeed and Glassdoor, and on Naukri and foundit for hiring in India. Where a board allows it, use your Career Page link as the apply link, so applicants land in the same Job Profile.

Referrals

Employee Referrals

Ask your leaders who they would hire again, then put referred candidates through the same assessment and interview as everyone else.

Senior engineers ignore generic recruiter messages. Lead with architectural scope, concrete hard problems, and a reference to their public work.

LinkedIn Post

We're hiring a Senior Backend Developer (FastAPI) to lead our Python platform architecture. If you've budgeted Postgres connections across pods and workers, replaced fire-and-forget BackgroundTasks with an outbox-backed queue, and migrated a fleet from Pydantic v1 to v2 without breaking clients — this role is built for you. Scope: technical direction for [N] FastAPI services on Kubernetes Stack: Python 3.12, FastAPI, Pydantic v2, SQLAlchemy 2.0 async, PostgreSQL + PgBouncer, Redis, Celery/ARQ, OpenTelemetry, AWS EKS Impact: shape platform standards, mentor engineers, own the hardest reliability problems Our senior process is respectful and practical — start with a 60-minute distributed-systems SkillSauce challenge: [SkillSauce Assessment Link] #FastAPI #Python #SoftwareArchitecture #AsyncIO #Microservices #StaffEngineer

Show template

Twitter / X Post

Show template

Know pods × workers × (pool_size + max_overflow) by heart? Built an outbox so webhooks never double-charge? We're hiring a Senior FastAPI Engineer to lead our async Python platform. 60-min practical, no trivia: [SkillSauce Assessment Link] #Python #FastAPI #Microservices

LinkedIn Direct Message

Hi [Candidate Name], I really enjoyed your [PyCon talk / blog post / Starlette or Pydantic contribution] on [topic] — especially your point about [specific detail]. I'm reaching out because [Company Name] is looking for a Senior Backend Engineer to set the technical direction for our FastAPI platform: [N] async services on Kubernetes, a queue-driven background processing layer we're hardening with the outbox pattern, and a real-time WebSocket product that needs to scale across pods. You'd have real architectural ownership and a team eager to learn from you. If you're open to it, I'd love to set up a confidential 30-minute conversation with our CTO. Our technical evaluation is a 60-minute practical system challenge rather than algorithm drills: [SkillSauce Assessment Link] Best regards, [Your Name]

Show template

Email Sequence for Senior FastAPI Candidates

Senior outreach should read like a peer-to-peer conversation: specific scope, honest challenges, a compact process, and an offer that spells out autonomy and impact.

Cold Sourcing

Subject: Lead the architecture of our FastAPI platform at [Company Name]

Show template

Hi [Candidate Name], I've been following your work on [Project/Talk/Article], and your perspective on [specific topic, e.g., running async SQLAlchemy behind PgBouncer at scale] is exactly what our team needs. [Company Name] runs [N] FastAPI services on Kubernetes handling [X] requests per day. Over the next year we're replacing in-process background tasks with an outbox-backed queue, standardizing tracing with OpenTelemetry, scaling our WebSocket notifications across pods, and finishing our Pydantic v2 migration. We're looking for a Senior Backend Developer to lead these decisions and mentor a team of [Team Size] Python engineers. Our senior process is focused and respectful of your time: a 60-minute SkillSauce distributed-systems exercise, a 25-minute TESSA AI conversation, and a system design deep dive with our CTO. Preview the challenge: [SkillSauce Assessment Link] Would you be open to a confidential conversation in the next couple of weeks? Best regards, [Your Name] [Your Title], [Company Name]

Interview Invite

Subject: Senior FastAPI Engineer — technical evaluation at [Company Name]

Show template

Hi [Candidate Name], Thank you for the conversation with [Hiring Manager Name]. We're excited to invite you to our technical evaluation, which you can complete within the next 5 business days: 1. SkillSauce Distributed Systems Assessment (60 mins): A three-service FastAPI system (orders, payments, notifications) with reliability problems. You'll implement a transactional outbox with a Celery or ARQ relay, idempotent webhook handling, a lifespan-managed database engine with a deliberate connection budget, and the proxy settings that keep WebSockets alive: [SkillSauce Assessment Link] 2. TESSA AI Interview (25 mins): A conversational interview on architectural decisions you've led, migrations, cross-team influence, and mentoring: [TESSA Interview Link] Afterwards, we'll schedule a 90-minute system design deep dive with our CTO and Principal Engineer, followed by a leadership panel. We look forward to it! [Your Name] [Company Name]

Offer Letter

Subject: Offer of Employment: Senior Backend Developer (FastAPI) at [Company Name]

Show template

Dear [Candidate Name], It's my great pleasure to offer you the position of Senior Backend Developer (FastAPI) at [Company Name]. Your outbox-based design in the SkillSauce assessment and your careful connection budgeting during the system design deep dive convinced the whole panel that you're the right person to lead our platform architecture. Offer Summary: • Position: Senior Backend Developer (FastAPI) • Base Salary: [Salary Amount] per annum • Equity: [Equity Grant] vesting over [Vesting Schedule] • Annual Bonus: [Bonus Target] • Start Date: [Start Date] • Reporting To: [CTO / VP Engineering Name] • Scope: Technical direction for the Python platform, including background processing, the service template, and real-time infrastructure • Learning & Speaking: [Amount] annual budget for conferences, speaking, and certifications • Equipment: Workstation of choice, PyCharm Professional, home-office stipend • Benefits: Premium health coverage, [PTO Days] days PTO, retirement matching, sabbatical eligibility after [N] years Please return the signed agreement by [Offer Expiration Date]. We're excited to build the next chapter of our platform with you. Warm regards, [Your Name] [Company Name]

Senior FastAPI SkillSauce & TESSA Assessment Blueprint

A chaos-graded distributed systems exercise paired with a leadership interview, designed to measure how a senior engineer designs async Python services for failure and drives adoption across teams.

60 mins

SkillSauce Senior FastAPI Distributed Systems & Resilience Assessment

Candidates receive a three-service FastAPI system running on Docker Compose with PostgreSQL, Redis, RabbitMQ, and Nginx. Confirmation emails sent via BackgroundTasks vanish when a container restarts, duplicate payment webhooks double-charge customers, a dependency that calls create_async_engine() per request exhausts database connections at 200 RPS, an outbound payment call has no timeout, and WebSocket clients disconnect after 60 seconds of silence. Candidates must add an outbox table written in the same transaction as the order and a worker relay, make the webhook idempotent on the provider event ID, move engine and httpx.AsyncClient creation into lifespan, set explicit timeouts with bounded retries, and fix the Nginx upgrade headers and read timeout. A chaos-test harness grades correctness under injected restarts, duplicate deliveries, and load.

Focus:: Transactional outbox with Celery or ARQ, idempotent webhooks, lifespan-managed resources and PostgreSQL connection budgeting, httpx timeouts and retries, WebSocket proxying, and graceful shutdown.

25 mins

TESSA AI Architecture & Technical Leadership Interview

TESSA runs an adaptive interview probing a major architecture decision the candidate led (e.g., a Flask-to-FastAPI migration, a sync-to-async data layer rewrite, or a Pydantic v2 upgrade across services), how they handled dissent, how they measured success, and how they grow senior engineers. Responses are evaluated for depth of reasoning, ownership of outcomes, and clarity for both technical and non-technical audiences. TESSA AI runs it as an AI interview and returns a scored report for the hiring team.

Focus:: Architectural decision-making, migration leadership, cross-team influence, mentoring, and communicating trade-offs to executives.

Why This Assessment Mix

Senior FastAPI engineers rarely fail on syntax; they fail on judgment — trusting in-process background work with money or customer emails, mis-sizing connections across a fleet of workers and pods, blocking the event loop with a dependency nobody profiled, or rolling out standards without bringing teams along. The 60-minute chaos-graded SkillSauce assessment shows how a candidate actually designs async Python services for failure, and TESSA's leadership interview evaluates the influence and communication skills that determine whether those designs get adopted. Together they predict architectural impact far more reliably than years of experience or framework buzzwords on a CV.

You can run tests like these in SkillSauce: build an IT skills assessment from the test library and turn on AI proctoring for remote candidates.

Architecture & Leadership Rubric for Senior FastAPI Engineers

These questions evaluate architectural judgment, migration leadership, influence without authority, and accountability — the traits that separate senior engineers from experienced individual contributors.

Tell me about the most significant architectural decision you drove on a Python API platform. What alternatives did you reject, and how did it play out?

Why it matters: Seniors are hired for judgment; the quality of their trade-off reasoning matters more than the outcome itself.
What to listen for: Green flags: explicit alternatives with criteria (e.g., async SQLAlchemy versus sync sessions in the threadpool, Celery versus ARQ versus a managed queue like SQS), a written ADR, load-test or production metrics before and after, and honest discussion of what they would change. Red flags: resume-driven choices ('async because it's faster', 'microservices because it's modern') or no measurement of outcomes.

How did you plan and execute a large migration, such as Flask or Django REST Framework to FastAPI, or Pydantic v1 to v2 across many services?

Why it matters: Migrations reveal planning skill, risk management, and the ability to coordinate many teams.
What to listen for: Green flags: inventory and sequencing, codemods such as bump-pydantic, the pydantic.v1 shim for incremental moves, OpenAPI schema diffing to catch contract changes, mounting the legacy WSGI app under FastAPI or a strangler-fig proxy for gradual cutover, and rollback plans. Red flags: a big-bang rewrite with no contract tests or rollback.

Two teams disagree about whether a new capability should be a separate FastAPI service or a router inside an existing one. How do you resolve it?

Why it matters: Tests influence without authority and principled decision-making.
What to listen for: Green flags: framing the decision around domain boundaries, data ownership, deployment cadence, scaling profile (e.g., CPU-heavy inference versus I/O-bound CRUD), and team topology; time-boxing a spike; documenting the decision and revisiting the criteria later. Red flags: deciding by seniority or avoiding the conflict.

How do you grow mid-level engineers into seniors on your team?

Why it matters: A senior engineer's leverage comes from multiplying the team.
What to listen for: Green flags: delegating design ownership with guardrails, structured design reviews, pairing on incidents such as event-loop stalls, sponsoring internal talks or RFCs, and concrete examples of people they helped promote. Red flags: hoarding critical work, or 'they should learn on their own'.

Describe a time a production incident exposed a flaw in an architecture you designed. What did you do?

Why it matters: Checks accountability and learning culture at the leadership level.
What to listen for: Green flags: owning the flaw openly, leading a blameless post-mortem, fixing the systemic issue (adding timeouts and bulkheads around outbound httpx calls, moving work to a durable queue, adding backpressure on WebSocket broadcasts), and sharing learnings across teams. Red flags: deflecting blame or only patching symptoms.

TESSA, the SkillSauce AI interviewer, can run a structured first-round interview built around questions like these. See how TESSA AI interviews work.

Verified Senior FastAPI Technical Questions

Five HARD-tier multiple-choice questions taken verbatim from the SkillSauce FastAPI question bank (it/question_bank/FastAPI.json), covering the production process model, durable background work, cached dependencies, dependency overrides in integration tests, and WebSocket troubleshooting behind a reverse proxy. Use each as a springboard for deeper architectural follow-ups.

When deploying a FastAPI application in a high-traffic production environment, which deployment strategy is best suited to handle a large number of concurrent connections?

Hard

aUse a synchronous WSGI server like uWSGI for all endpoints
bDeploy with Gunicorn using Uvicorn workers configured for asynchronous processingCorrect
cRely solely on horizontal scaling without optimizing the server configuration
dRun the FastAPI app using its built-in development server with multiple threads

Explanation: Using Gunicorn with Uvicorn workers leverages asynchronous processing, which is essential for handling many concurrent connections in production environments.

Why it matters: Evaluates the production process model. Use it as a springboard: Gunicorn supervising Uvicorn workers is the classic answer for VMs, but on Kubernetes many teams run a single Uvicorn process per container and scale with replicas. Probe worker count per core, --timeout and graceful shutdown, --proxy-headers/--forwarded-allow-ips behind a load balancer, and how worker count multiplies database connections.

You're implementing a background task in FastAPI to send emails asynchronously after an API call. Which approach offers the most robust and production-ready solution?

Hard

aExecute email sending directly within the request handler using async functions
bRely solely on a thread pool executor invoked inside the endpoint
cUse FastAPI's BackgroundTasks to perform email sending immediately after the response
dIntegrate Celery with FastAPI to offload email sending to a dedicated worker processCorrect

Explanation: Integrating Celery allows email tasks to be processed independently from the request-response cycle, ensuring reliability and scalability in production.

Why it matters: Tests whether the candidate knows BackgroundTasks is in-process and not durable. Seniors should go further: enqueue only after the database transaction commits (or via a transactional outbox) to avoid emailing about rolled-back orders, configure retries with backoff and acks_late, make tasks idempotent, and weigh Celery against ARQ, Dramatiq, or SQS for an async-first stack.

In a microservices environment, you need to securely manage and reuse a token retrieved from environment variables across multiple FastAPI endpoints. Which method best ensures thread safety and efficient reuse?

Hard

aImplement a dependency function with an LRU cache decorator to retrieve and cache the tokenCorrect
bUse a dependency function with a yield statement and context management for cleanup
cInstantiate a new token retrieval process within each endpoint call
dStore the token in a global variable and access it directly in your endpoints

Explanation: Caching the token retrieval using an LRU cache ensures thread safety and avoids unnecessary recomputation, making it efficient for production use.

Why it matters: Mirrors FastAPI's documented @lru_cache get_settings() pattern with pydantic-settings, injected through Depends so tests can override it. Push deeper: lru_cache caches forever, so rotating secrets or short-lived OAuth client-credentials tokens need a TTL-aware cache with refresh-before-expiry and an asyncio.Lock to avoid thundering-herd refreshes.

When writing complex integration tests that simulate production behavior in a FastAPI application, which strategy would be most effective to properly override dependencies?

Hard

aRely exclusively on unit test mocks without using dependency overrides
bTest against a live production server to capture all dependency nuances
cCall endpoint functions directly without considering dependency injection
dUse FastAPI's TestClient with dependency override mechanisms to simulate production dependenciesCorrect

Explanation: Using TestClient with dependency overrides allows you to accurately simulate production scenarios while maintaining test isolation and control.

Why it matters: Evaluates test architecture: app.dependency_overrides to swap auth, external clients, and database sessions, cleared after each test; per-test transactions rolled back against a real PostgreSQL container; and httpx.AsyncClient with ASGITransport for async tests (with a lifespan manager when startup resources are needed, since the transport does not run lifespan on its own).

Your FastAPI application uses WebSockets for real-time updates in a microservice architecture, but you are experiencing intermittent connection drops. What is the most prudent first step to troubleshoot this issue?

Hard

aConvert WebSocket endpoints to standard HTTP endpoints
bImplement caching middleware to stabilize the connections
cExamine the reverse proxy configuration (e.g., Nginx) to ensure proper WebSocket supportCorrect
dIncrease FastAPI's internal logging level to trace WebSocket connections

Explanation: Issues with intermittent WebSocket connection drops are often related to misconfigurations in reverse proxies that do not properly support WebSocket protocols, making this the most effective troubleshooting step.

Why it matters: Probes operational depth with real-time traffic: Nginx needs proxy_http_version 1.1 plus Upgrade/Connection headers, and its default 60-second proxy_read_timeout (or a cloud load balancer's idle timeout) silently kills quiet sockets. Seniors also discuss application-level heartbeats, client reconnect with backoff, and draining connections during rolling deploys.

Need more questions for this stack? Browse sample questions by skill in the SkillSauce Question Library.

High-Touch Rejection Templates for Senior FastAPI Candidates

Senior candidates often become future hires, partners, or referrers in a tight-knit Python community. Close the loop personally, acknowledge their investment, and offer a follow-up conversation.

Post-Assessment Rejection

Subject: Your Senior Backend Developer (FastAPI) application at [Company Name]

Show template

Hi [Candidate Name], Thank you for investing an hour in our SkillSauce distributed systems assessment for the Senior Backend Developer (FastAPI) role. We know senior candidates have limited time, and we don't take that lightly. After a careful review of your SkillSauce report by our Principal Engineer, we've decided not to proceed. The role requires immediate leadership on [e.g., durable background processing and failure handling], and your submission left gaps in [e.g., webhook idempotency under duplicate deliveries / keeping database connections within budget under load] that we couldn't resolve in the time available. We recognize a timed exercise captures only part of your experience. We'd welcome the chance to reconnect for future senior or staff openings. Thank you again for your time and thoughtfulness. Kind regards, [Your Name] [Company Name]

Post-Interview Rejection

Subject: Thank you — Senior Backend Developer (FastAPI) at [Company Name]

Show template

Hi [Candidate Name], Thank you for the depth and generosity you brought to our process — the system design session with [Interviewer Name] sparked a lot of discussion on our team, especially your thinking on [specific strength, e.g., fanning out WebSocket messages across pods with Redis pub/sub]. This was one of our hardest decisions, but we've chosen to move forward with a candidate whose background more closely matches our immediate priority of [specific need, e.g., leading our sync-to-async data layer migration]. That reflects our current needs, not a lack of capability on your part. If you're open to it, I'd value staying connected — and I'd be glad to share more detailed feedback in a short call. We hope our paths cross again. Warm regards, [Your Name] [Company Name]

Put This Guide to Work

SkillSauce tools that cover the steps above when you hire a Backend Developer (FastAPI).

See how SkillSauce fits your hiring process

Book a demo with the team, or start a free trial.

Book a demo Start free trial

Hiring Workflow, Step by Step

Open a Job Profile for the role

Fill the candidate pool

Shortlist the strongest resumes

Test the skills the role needs

Interview with TESSA AI

Meet the shortlist

Decide together and make the offer

Company Fit: Startups, SMBs and Enterprises

Startups

SMBs

Enterprises

Junior Backend Developer (FastAPI)

How to Hire a Junior Backend Developer (FastAPI)

Define a Realistic Junior Scope

Source From Python Learning Communities

Screen With a 30-Minute SkillSauce Practical Assessment

Run a 15-Minute TESSA AI Interview

Hold a 45-Minute Pair-Debugging Session

Extend an Offer With a Mentorship Plan

Pro Tips

Junior Backend Developer (FastAPI) Job Description Template

Role Overview

First 90 Days Deliverables

Must-Have Requirements

Nice-to-Have Skills

Benefits & Mentorship

Sourcing Channels for Early-Career FastAPI Talent

Job Boards

Campus Placement Cells and Referrals

Social Outreach Copy for Junior FastAPI Developers

LinkedIn Post

Twitter / X Post

LinkedIn Direct Message

Email Sequence for Junior FastAPI Candidates

Cold Sourcing

Interview Invite

Offer Letter

Junior FastAPI SkillSauce & TESSA Assessment Blueprint

Why This Assessment Mix

Behavioral & Fundamentals Rubric for Junior FastAPI Developers

Walk me through what happens when a JSON POST request hits a FastAPI endpoint, from Uvicorn receiving it to the response being sent.

Tell me about a bug in one of your Python projects that took you more than a day to solve. How did you eventually find it?

When would you write a path operation with async def and when with plain def?

Describe a time you received critical feedback on your code. What did you change?

Verified Junior FastAPI Technical Questions

In FastAPI, which decorator is used to define a route that handles GET requests?

If you need to validate incoming JSON data in a FastAPI endpoint, which tool should you use?

When preparing to deploy a FastAPI application in production, which ASGI server is commonly recommended?

How can you implement background tasks in FastAPI to perform operations after sending the response?

What is the simplest way to implement global exception handling in a FastAPI application?

Constructive Rejection Templates for Junior FastAPI Candidates

Post-Assessment Rejection

Post-Interview Rejection

Mid-Level Backend Developer (FastAPI)

How to Hire a Mid-Level Backend Developer (FastAPI)

Scope the Role Around Service Ownership

Source Engineers With Production FastAPI Footprints

Administer a 45-Minute SkillSauce Async Performance & Security Assessment

Use a 20-Minute TESSA AI Interview for Ownership & Trade-Offs

Run a 60-Minute Live Code Review & Design Session

Close With a Competitive, Ownership-Focused Offer

Pro Tips

Mid-Level Backend Developer (FastAPI) Job Description Template

Role Overview

First 90 Days Deliverables

Must-Have Requirements

Nice-to-Have Skills

Benefits & Growth

Targeted Sourcing Channels for Mid-Level FastAPI Engineers

Job Boards

Employee Referrals

Social Outreach Copy for Mid-Level FastAPI Engineers

LinkedIn Post

Twitter / X Post

LinkedIn Direct Message

Email Sequence for Mid-Level FastAPI Candidates

Cold Sourcing

Interview Invite

Offer Letter

When would you write a path operation with `async def` and when with plain `def`?