Hire Python developers for AI, data and the APIs behind them
Python engineers who take machine learning out of notebooks, build FastAPI and Django services, and run the data pipelines that feed both. They work in your repository and your standups, and are tested on the kind of Python your project needs.
- Vetted in four stages
- In place within days
- Your codebase, your tools, your standups
- Code and IP transferred to you
- One UK company accountable
What our Python developers build
- Machine learning pipelines that run without a personTraining, evaluation and serving code that runs on a schedule rather than on someone's laptop. Models are versioned, inputs are checked, and a retrain can be repeated from the same data.
- FastAPI and Django servicesTyped APIs with Pydantic models and async endpoints where the load needs them. Django where its admin, auth and ORM save weeks on an internal tool.
- Data engineeringIngestion jobs, transformations in pandas or Polars, and orchestration in Airflow or Dagster. Each job tests what arrives, so a broken feed stops loudly instead of filling a report with gaps.
- Computer vision with PyTorchDetection, classification and inspection models trained on your own images. They are served behind an API or on the device in the field, with the labelling and evaluation work done properly first.
- Automation of manual workScripts and small services that replace the weekly export, the copy between two systems and the folder of files someone processes by hand. They log what they did, and say clearly when they could not.
Python skills we vet for
Python and services
- Python 3 with type hints
- FastAPI
- Django and Django REST Framework
- Pydantic
- Celery
- SQLAlchemy and PostgreSQL
- pytest
Data engineering
- pandas
- Polars
- Airflow
- Dagster
- Data quality tests
- Warehouse loading
Machine learning
- PyTorch
- scikit-learn
- Computer vision
- Forecasting
- MLflow
- MLOps
LLM applications
- Retrieval-augmented generation
- Embeddings and vector search
- Evaluation sets
- AI agents and tool calling
- Workflow automation
What the practical task checks for Python
- Readable, typed Python: type hints that a checker accepts, small functions and clear module boundaries.
- Messy data handled honestly: a file with missing fields and bad dates, cleaned without silently dropping rows.
- Knowing when async helps: where asyncio speeds up an API, and where one blocking call would stall it.
- Tests in pytest that cover the edge case the brief hints at, not only the happy path.
- For machine learning roles: a train and test split that does not leak, and a plain reading of what the evaluation metric says.
How we vet Python developers
Four stages, before anyone joins your work. You meet the people we propose before anything is signed.
- Portfolio & experience reviewRelevant projects, technical depth and hands-on experience are reviewed first.
- Technical assessmentA practical task tests code quality, problem-solving and the stack the project uses.
- Quality & team reviewSenior engineers review the work for implementation quality, communication and collaboration.
- Final approvalConfirmed against the project's requirements and Backendz's quality standards before joining a team.
Engage the way the work needs
- Full-time on your teamAn engineer who works with you every day, in your repository, your tickets and your standups. They take work from your backlog like anyone else on the team.
- Part-timeA share of an engineer's week for work that does not need a full-time seat, such as keeping an integration running or a steady flow of smaller changes.
- For a sprintAn engineer or two for a defined piece of work: a feature, a migration step, an audit. The scope is agreed up front and the work is handed back at the end.
- For the long termA dedicated team that grows with your roadmap. The same engineers stay on your product, so what they learn about it stays with you.
Hiring Python developers: questions we are asked
Do your Python developers do machine learning, or only web work?
Both, and we match the engineer to the work. Some of our Python engineers build FastAPI and Django services; others build ML pipelines, computer vision models and LLM applications, and the technical task we set reflects which one your project needs.
Should we use FastAPI or Django?
FastAPI for a lean, typed API service; Django when you want an admin, auth and an ORM from the first day. Many teams run both: Django for the internal tool, FastAPI for the service that answers other systems.
Can a Python developer take our model from a notebook into production?
Yes, and that is a large part of the work. They turn the notebook into versioned training code, add checks on the input data, serve the model behind an API, and set up monitoring so you can see when its predictions drift.
Can they build LLM and RAG features?
Yes. Our Python engineers build retrieval-augmented generation, AI agents and LLM features, with an evaluation set written early so changes to prompts or models are measured rather than guessed.
How do you test a Python developer before they join?
With a practical task in Python built around your project's work. It checks code quality, typing, testing and how they deal with untidy data, and senior engineers review the result before anyone is put forward.
Still running on workarounds?
Book a 30-minute call with an engineer. Tell us what's breaking and we'll tell you what we would build, how long it would take and what it would cost, before you commit to anything.
