What FastAPI Developers Actually Build
FastAPI is specifically designed for building APIs—REST APIs, GraphQL APIs, and microservices. Here's where FastAPI developers work:
High-Performance APIs
FastAPI's async support makes it ideal for:
- Real-time APIs - WebSocket support, streaming responses
- High-concurrency services - Handle thousands of requests per second
- Microservices - Lightweight, fast startup times
- Internal tools - Microsoft, Netflix, Uber use it for internal APIs
Machine Learning Model Deployment
FastAPI is particularly popular for ML/AI:
- Model serving - Expose ML models as REST APIs
- Inference endpoints - Fast response times for predictions
- ML pipelines - Integrate with PyTorch, TensorFlow, scikit-learn
- Data science APIs - Serve data processing endpoints
Companies: Many ML teams choose FastAPI over Flask for better performance and automatic docs.
Modern Python Backends
FastAPI represents the modern Python stack:
- Type-safe APIs - Pydantic validation catches errors early
- Auto-generated docs - OpenAPI/Swagger without extra work
- Async-first - Built for modern async Python patterns
- Dependency injection - Built-in DI system for clean architecture
Why FastAPI Over Django/Flask?
vs Django
- Django: Full-featured framework ("batteries included") for full-stack apps
- FastAPI: Lightweight, API-focused, better async support
- Use FastAPI when: Building APIs only, need high performance, want automatic docs
- Use Django when: Building full-stack apps with admin panels, need Django's ecosystem
vs Flask
- Flask: Minimal, flexible, but synchronous by default
- FastAPI: Modern async, automatic validation, better performance
- Use FastAPI when: Need async support, want type safety, building production APIs
- Use Flask when: Need maximum flexibility, building simple APIs, team prefers Flask
Reality: Many teams migrate from Flask to FastAPI for better performance and developer experience.
The Modern FastAPI Developer Profile
Async Python Mastery
FastAPI is async-first. Strong candidates understand:
- async/await - Not just syntax, but when to use it
- Concurrency patterns - Handling multiple requests efficiently
- Async libraries - httpx, asyncpg, aioredis
- Event loops - Understanding asyncio fundamentals
Red flag: A candidate who's only written synchronous Python won't leverage FastAPI's strengths.
Type Hints & Pydantic
FastAPI uses Python type hints extensively:
from pydantic import BaseModel
class UserCreate(BaseModel):
email: str
age: int
@app.post("/users/")
async def create_user(user: UserCreate) -> User:
...
Evaluation: Check if their code uses type hints. FastAPI developers should write type-safe code.
API Design Principles
FastAPI developers should understand:
- RESTful design - Resource modeling, HTTP methods
- OpenAPI standards - Understanding the generated docs
- Error handling - Proper status codes, error responses
- Versioning - API versioning strategies
Common Hiring Mistakes
1. Treating FastAPI Like Django
FastAPI is NOT Django. Don't ask about Django ORM, admin panels, or Django-specific patterns. FastAPI developers work with SQLAlchemy (async), databases directly, or async ORMs.
Fix: Focus on API design, async patterns, and Python type hints.
2. Ignoring Async Experience
FastAPI's main advantage is async support. A candidate who's only written synchronous Python will struggle.
Fix: Ask about async/await experience. "Tell me about an async Python project you've worked on."
3. Overemphasizing Framework Experience
FastAPI is relatively new (2018). A strong Python developer with async experience can learn FastAPI in 1-2 weeks.
Fix: Test Python fundamentals and async understanding, not FastAPI trivia.
4. Missing the ML Connection
Many FastAPI roles involve ML model deployment. If that's your use case, assess ML integration experience.
Fix: Ask about deploying ML models or working with data science teams.
Recruiter's Cheat Sheet
Resume Green Flags
- Async Python experience (asyncio, aiohttp, asyncpg)
- Type hints in code samples
- API design experience (REST, OpenAPI)
- ML/deployment experience (if relevant)
- Performance optimization mentions
- Open source FastAPI projects
Resume Yellow Flags
- Only Django/Flask experience without async
- No type hints in code
- Generic "Python developer" without specifics
- No API design experience
Technical Terms to Know
| Term | What It Means |
|---|---|
| Pydantic | Data validation library using Python type hints |
| OpenAPI/Swagger | API documentation standard (auto-generated by FastAPI) |
| async/await | Python syntax for asynchronous programming |
| Dependency Injection | FastAPI's built-in DI system for managing dependencies |
| Starlette | Web framework FastAPI is built on |
| Uvicorn | ASGI server for running FastAPI apps |
Questions That Reveal Skill Level
| Question | Junior Answer | Senior Answer |
|---|---|---|
| "Why use FastAPI over Flask?" | "It's faster" | Explains async benefits, type safety, automatic docs, performance benchmarks |
| "How do you handle database connections?" | "I use SQLAlchemy" | Discusses asyncpg, connection pooling, async context managers, transaction handling |
| "Explain dependency injection in FastAPI" | "It's for dependencies" | Shows practical examples, discusses testing benefits, architecture patterns |
Skills Assessment by Use Case
If You're Building High-Performance APIs
- Priority: Async patterns, performance optimization, concurrency
- Interview focus: "How would you handle 10,000 concurrent requests?"
- Red flag: No understanding of async or performance implications
If You're Deploying ML Models
- Priority: ML integration, model serving, inference optimization
- Interview focus: "How would you expose a PyTorch model as an API?"
- Red flag: No ML or deployment experience
If You're Building Microservices
- Priority: API design, service communication, error handling
- Interview focus: "Design an API for [your service]"
- Red flag: Can't design RESTful APIs or handle errors properly