About the service
I design and build production backends on FastAPI and believe speed and type safety are the baseline, not a nice addition. Every endpoint I deliver is described by Pydantic models, so request and response format is validated at entry and documented automatically. You get interactive OpenAPI documentation out of the box: frontend teams, mobile developers and integration partners read a live contract, not guesses about fields. I use FastAPI's dependency injection to keep database sessions, configuration and current user definition clean and testable, and code remains readable as the project grows.
Asynchronicity is what FastAPI is taken for, and this is where I work best. I write non-blocking handlers on top of async database drivers, HTTP clients and message queues, so one worker serves multiple parallel requests without getting stuck on I/O. This makes the stack a natural choice for serving ML models and high-load Python backends: inference, batch predictions and heavy computations I move to background tasks or separate workers so the request path stays responsive. Authorization I do properly — OAuth2 and JWT, limited permissions, password hashing, and I add rate limiting and structured logging so you really see what happens in production.
You get not just a set of routes. I deliver a documented, tested service with clear project structure, migrations, environment configuration and a container image ready for deployment.
— Pydantic v2 schemas with request and response validation
— Async SQLAlchemy or async drivers plus migrations
— OAuth2 or JWT authorization, scopes and rate limiting
— Background tasks and workers for ML serving and long operations
— Dockerfile, tests and configured CI
Asynchronicity is what FastAPI is taken for, and this is where I work best. I write non-blocking handlers on top of async database drivers, HTTP clients and message queues, so one worker serves multiple parallel requests without getting stuck on I/O. This makes the stack a natural choice for serving ML models and high-load Python backends: inference, batch predictions and heavy computations I move to background tasks or separate workers so the request path stays responsive. Authorization I do properly — OAuth2 and JWT, limited permissions, password hashing, and I add rate limiting and structured logging so you really see what happens in production.
You get not just a set of routes. I deliver a documented, tested service with clear project structure, migrations, environment configuration and a container image ready for deployment.
— Pydantic v2 schemas with request and response validation
— Async SQLAlchemy or async drivers plus migrations
— OAuth2 or JWT authorization, scopes and rate limiting
— Background tasks and workers for ML serving and long operations
— Dockerfile, tests and configured CI
