Technology
Python Development Services — Backend, AI/ML, Data & Automation
Production Python engineering — FastAPI services, async pipelines, AI/ML workloads, data engineering at scale, and the typed, tested, observable discipline production Python deserves.
What we build with Python
- FastAPI and Starlette services with async I/O, OpenAPI-first design, and Pydantic v2 schemas
- Django and Django REST Framework for product-grade backends
- AI / ML workloads with PyTorch, scikit-learn, transformers, LangChain, and LangGraph
- Data pipelines with Airflow, Prefect, and Dagster — orchestrated, observable, and recoverable
- Strict typing with mypy or pyright, enforced in CI — no untyped code lands on main
- Modern packaging with uv or poetry, reproducible Docker builds, and lockfile hygiene
- Background processing with Celery, RQ, Dramatiq, or arq
- Database integration with SQLAlchemy, Tortoise, Prisma Client Python, or async drivers (asyncpg, motor)
- Observability with OpenTelemetry traces, structured logs, and Prometheus metrics
- Deployment to AWS Lambda, ECS, EKS, or Cloud Run — depending on workload shape
- Migration paths from Python 2 to 3, from Flask to FastAPI, or from untyped to fully typed Python
- Performance tuning: async correctness, asyncio profiling, and CPU-bound delegation to Rust/Cython where it pays off
Why DiveScale
Built by engineers who ship Python in production
Python is the default backbone for AI / ML, data engineering, and fast-moving APIs. DiveScale ships production Python with the discipline you would expect of a typed compiled stack — strict typing, async correctness, observability, and CI gates that block regressions before they reach main.
We work across the spectrum: FastAPI services holding tens of thousands of concurrent connections, Django apps anchoring full SaaS products, and data pipelines moving terabytes a day on Airflow / Prefect / Dagster. Whichever shape the workload takes, we build for change — typed seams, modular services, and tests that catch real regressions.
On the AI side, Python is increasingly the lingua franca for LLM orchestration. We build agent frameworks with LangChain and LangGraph, RAG pipelines with proper chunking and re-ranking, eval harnesses with golden datasets, and production wiring that handles retries, fallbacks, and provider routing.
We wire Python sensibly into the rest of the stack: TypeScript clients with generated types from OpenAPI; Terraform-managed deployment; containerization that scales horizontally on Kubernetes or serverless on Lambda; structured logs and OpenTelemetry traces so the boring parts of operations actually work.
And we take over struggling Python codebases honestly. A 2-week audit, quick wins shipped in the first month (typing, tests, slowest queries fixed), and a 3-month plan to bring the codebase to production discipline without freezing feature work. No big-bang rewrites.
Python use cases we deliver
How we deliver
Our Python delivery process
- 01
Stack audit & target
We map the current architecture and target a typed, tested, deployable shape — even if we are starting from a notebook or a 7-year-old Flask app.
- 02
Foundation: typing + tests
Strict typing with mypy or pyright in CI, pytest with coverage gates, and lockfile hygiene. Non-negotiable foundation before feature work.
- 03
Build with observability
OpenTelemetry traces, structured logs, metrics on real failure modes, health checks. Production from day one.
- 04
Deploy & scale
Containerized deploys to Kubernetes / ECS / Lambda / Cloud Run — chosen per workload, not per ideology.
- 05
Performance pass
Async correctness review, asyncio profiling, query plan tuning, and CPU delegation to Rust or Cython for the hot paths that justify it.
- 06
Operate or hand off
We can stay on for ongoing platform engineering, or hand off to your team with runbooks and a clean codebase they can confidently extend.
Related technologies
Django
Production Django and Django REST Framework — admin-heavy products, typed services, and the operational layer enterprise teams expect.
Learn moreJavaScript
Production JavaScript engineering across modern web frameworks, Node services, and edge runtimes — fluent in the ecosystem and disciplined about its sharp edges.
Learn moreTypeScript
End-to-end typed engineering — React, Next.js, NestJS, Node, and shared schemas — with the discipline TypeScript was built for.
Learn moreNode.js
Production Node.js engineering — NestJS, Fastify, Hono, real-time systems, job queues, and the operational discipline that single-threaded runtimes demand.
Learn morePython — Frequently Asked Questions
FastAPI for async APIs, AI workloads, and OpenAPI-driven development. Django for admin-heavy products and rapid CRUD. Starlette when we want FastAPI internals without the magic. Flask only for legacy maintenance. We pick per project, not per preference.
