Python BackendData pipelines, APIs, and AI integrations
From FastAPI microservices to data engineering pipelines and LLM integrations, we use Python wherever it shines. Rapid prototyping to production-hardened services.
Python is the world's most popular programming language — powering everything from web APIs and data pipelines to machine learning models and automation scripts. Its readability, vast ecosystem (PyPI has 500K+ packages), and first-class support for AI/ML make it indispensable for modern backend development. At A Major, we use Python for FastAPI microservices, Django applications, data engineering workflows, and LLM integrations. Python's strength is versatility. The same language that serves your REST API can train your ML model, process your data pipeline, and automate your infrastructure. We bring production engineering discipline to Python projects — type hints, async I/O, containerised deployments, and comprehensive test suites.
Quick start
uv init my-project
cd my-project
uv add fastapi uvicorn
uv run uvicorn main:app --reloadRead the full documentation at docs.python.org/3/
FastAPI async services
Async I/O, automatic OpenAPI docs, and Pydantic validation — high-throughput APIs with minimal code.
Django REST Framework
Feature-complete backends with viewsets, serialisers, and permissions for content-heavy applications.
Data pipelines
ETL and ELT workflows with Pandas, Polars, or dbt — from raw sources to clean analytical models.
LLM integrations
Anthropic, OpenAI, and LangChain integrations — RAG pipelines, agents, and structured output.
ML model serving
Model inference endpoints with sub-50ms latency using FastAPI, BentoML, or Ray Serve.
Celery task queues
Distributed async task processing with Celery and Redis or RabbitMQ — retries, priorities, scheduling.
Why it's hard
GIL and concurrency limitations
Python's Global Interpreter Lock limits CPU-bound parallelism. Async I/O, multiprocessing, or offloading to compiled extensions are required for compute-heavy workloads.
Dependency management complexity
Virtual environments, pip, Poetry, and uv all solve dependency management differently. Choosing and standardizing on one approach across a team is critical.
Type safety in large codebases
Python's dynamic typing becomes a liability at scale. Gradual typing with mypy or pyright requires discipline but is essential for maintainable systems.
Production deployment patterns
Choosing between WSGI (Gunicorn), ASGI (Uvicorn), and serverless runtimes affects performance characteristics and scaling behaviour significantly.
Best practices
Use uv for fast dependency management
uv is a Rust-based pip replacement that resolves and installs dependencies 10–100× faster than pip.
Add type hints to all public APIs
Type hints enable IDE autocompletion, catch bugs early with mypy, and serve as living documentation.
Use Pydantic for data validation
Pydantic models validate input data, serialize output, and generate JSON Schema — one library for validation everywhere.
Structure projects with src layout
The src/ layout prevents accidental imports of uninstalled code and makes packaging straightforward.
Frequently asked questions
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