Python

My expertise with Python spans AI systems, RESTful and MCP service surfaces, and the integration work that joins them. In practice that means async FastAPI services — the retrieval platform I built at Spotware runs under Gunicorn with Uvicorn workers on Kubernetes — LangChain and LangGraph state graphs driving multi-step retrieval and query-transformation pipelines (and the agents themselves in the corporate agent platform I am building), FastMCP for the agent-facing tool servers layered on top, and Pydantic validating each boundary, including the one where a language model hands back a response and it has to be a schema, not a guess.

I consistently apply advanced Python techniques including asynchronous programming, composed application lifespans that share pooled database connections and long-lived clients across mounted sub-applications, caching strategies, object-oriented design, and containerization with Docker. Quality is enforced rather than assumed: pytest suites that mock the language model and the database calls so the tests measure my code and not the vendor's, strict type checking with mypy and pyright, Ruff linting behind pre-commit hooks, and CI that has to pass on every pull request. Where the work is open source I own the packaging and release automation as well — both MCP Context Server and st-copy are built and published to PyPI straight from that pipeline.

Detailed examples of my Python-driven AI projects can be viewed on the AI Product Manager at Spotware page.

Much of that Python work is public — Python leads the language split in my GitHub activity, summarized here from live data at build time: