Python
Python is the language of the AI era, which means your company now runs on more of it than anyone planned — some written by engineers, some by data scientists, some by a model, and an uncomfortable amount promoted to production because the notebook worked once. The language is fine. The engineering discipline around it is the variable.
We write Python like the production language it has become: typed with mypy and enforced in CI, structured for testing rather than retrofitted with mocks, async where I/O concurrency pays and boring where it doesn't, profiled before optimized, and packaged reproducibly with modern tooling. From FastAPI services to agent runtimes to the data pipelines between them — senior Python, shipped with the habits that survive on-call.
Production AI services
Agent runtimes, RAG backends, and inference services — typed, tested, observable.
FastAPI backends
High-concurrency async APIs with real validation, auth, and OpenAPI contracts.
Research-to-production hardening
Notebook logic rebuilt as tested, deployable services without losing the science.
Performance engineering
Profiling-driven fixes — async I/O, batching, native extensions where they genuinely pay.
Tooling modernization
uv, ruff, mypy, and CI discipline — dependency chaos replaced with reproducible builds.
Is Python too slow for production services?
Rarely the real story. Most Python services are I/O-bound, where async concurrency shines; hot CPU paths get profiling and targeted fixes — vectorization, native libraries, or a small compiled extension. Teams that rewrite everything in Go for speed usually needed a profiler, not a new language.
Our data scientists' code runs production workloads. Is that bad?
It's normal, and it's a risk exactly proportional to the missing engineering: no types, no tests, no error handling, single-threaded assumptions. We harden it collaboratively — keeping the logic scientists own, wrapping it in the discipline production demands. Nobody's notebook gets shamed; it gets shipped.
Which Python web framework should we standardize on?
FastAPI for API services, in almost every modern case: async-native, typed, self-documenting. Django when you want its batteries — admin, ORM, auth — for product backends. The wrong answer is three frameworks across five teams, which is usually what we're consolidating.
Put Python to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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