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●Technology Expertise

Python & FastAPI
Development.

Expert Python & FastAPI development with deep production experience. From architecture decisions to performance optimization, I help teams build systems that scale.

fastapi developerpython api developerpython backend developerfastapi consultantpython ml engineerasync python expert

●Expertise Level

Building FastAPI services since 0.60.0 (2020), contributed to open-source FastAPI projects. Expert in async Python patterns, GPU memory management for ML inference, and building systems that handle thousands of concurrent connections. Deployed FastAPI services processing 50M+ API calls monthly.

●When to Use Python & FastAPI

Building high-performance async APIs that need to handle 10K+ concurrent connections with minimal resource usage

AI/ML inference servers where Python's ecosystem (PyTorch, TensorFlow, transformers) is non-negotiable

Data processing pipelines that benefit from NumPy, Pandas, and scientific computing libraries

Projects requiring automatic OpenAPI/Swagger documentation generated from type hints

Microservices that need sub-millisecond routing overhead (FastAPI uses Starlette, one of the fastest ASGI frameworks)

Teams with data scientists who need to deploy models without learning a new language

Real-time applications using WebSockets with proper async handling and connection management

●Best Practices

Use async def for I/O-bound endpoints (database, HTTP calls) and def for CPU-bound operations (FastAPI handles threading)

Implement proper dependency injection for database sessions, authentication, and rate limiting—makes testing trivial

Structure projects with routers (APIRouter) for domain separation: /api/v1/users, /api/v1/auth, /api/v1/ml

Use Pydantic's Field() for validation constraints, examples, and OpenAPI schema customization in one place

Implement background tasks for non-blocking operations; use Celery/Redis for anything taking >30 seconds

Set up proper logging with structlog or loguru, including request IDs for distributed tracing

Use httpx.AsyncClient for outbound HTTP with connection pooling instead of requests (which blocks)

●Common Pitfalls to Avoid

Blocking the event loop with synchronous code—use run_in_executor or dedicated thread pools for CPU-bound work

Not understanding that FastAPI's dependency injection runs per-request; use lifespan handlers for app-level resources

Forgetting that Pydantic v2 has breaking changes from v1—model_dump() replaces dict(), model_validate() replaces parse_obj()

Using global database connections without proper async session management causes connection pool exhaustion under load

Deploying with uvicorn --reload in production instead of gunicorn with uvicorn workers for proper process management

Not setting up proper CORS middleware early—preflight requests fail silently, causing confusing frontend errors

Ignoring the GIL for CPU-bound ML inference—use multiprocessing, Celery workers, or dedicated inference servers

●Ideal Project Types

AI/ML inference APIs and model serving
Data processing and ETL pipelines
Real-time WebSocket applications
High-throughput microservices
Scientific computing backends
Automation and scraping orchestration

●Complementary Technologies

PyTorch/TensorFlow (ML model training and inference)
Celery + Redis (distributed task queues)
SQLAlchemy 2.0 or Tortoise ORM (async database)
Pydantic (data validation and serialization)
Docker (containerization for ML dependencies)
NVIDIA Triton (production ML inference)

●Real-World Example

Case Study

PhotoKeep Pro's backend exemplifies production FastAPI architecture. The service orchestrates 14 deep learning models (SUPIR, HAT, CodeFormer, GFPGAN, DDColor) on a 49GB VRAM GPU, requiring careful memory management. I implemented a lazy-loading model registry with LRU eviction—models load on first request and unload when VRAM pressure exceeds 80%. The API uses FastAPI's dependency injection for GPU semaphores, ensuring only one inference runs per model at a time to prevent OOM crashes. Background tasks handle the actual processing: the endpoint returns a job ID immediately, Celery workers pick up the task, and clients poll or receive webhooks on completion. For real-time progress, I implemented SSE (Server-Sent Events) endpoints streaming processing stages. The result: 99.95% uptime, 28.5dB PSNR restoration quality (beating Magnific AI), and P95 latency under 200ms for the API layer while ML inference runs asynchronously.

●Related Services

●Ready to Build?

Let's discuss your
Python & FastAPI project.

Whether you're starting fresh, migrating an existing system, or need architectural guidance, I can help you build with Python & FastAPI the right way.

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