Backend services
Django and FastAPI applications with proper migrations, authentication and admin tooling. The framework is chosen for the job, not for preference.
Backends, data and automation
Hire Python Developers
Python engineers who build services and move data. Django and FastAPI applications, pipelines that run on a schedule, and the model integration work that has to behave predictably in production.
What our Python Developers do
Python covers two quite different jobs, and it is worth being clear which one you are hiring for. Building services with Django or FastAPI is one. Moving and shaping data, pipelines on a schedule, integrations that have to be reliable at three in the morning, is another.
The engineers are not always interchangeable, and a mismatch here shows up as a project that is technically staffed and somehow not progressing. We ask which of the two it is before proposing anybody.
For work that touches models or retrieval, our AI capability page sets out how we scope it, including how often the right answer turns out to be a simpler system.
What they build
The work this role actually does here, on client products that are live.
Data pipelines
Extraction, transformation and loading between systems that were never designed to talk. Scheduled, monitored, and able to be re-run from any point when a source is late.
Model integration
Putting language models and retrieval into a product: prompt handling, evaluation, caching and a fallback for when the provider is down. Engineering work, not experimentation.
Automation
The recurring manual process somebody does every Monday. Replaced with something that runs on its own and tells you when it did not.
What they know
Named rather than implied, so you can check it against your own job description before we talk.
Frameworks
- Django
- FastAPI
- Flask
- Celery
- Django REST Framework
Data
- PostgreSQL
- pandas
- Airflow
- dbt
- Redis
Retrieval
- LangChain
- pgvector
- Pinecone
- OpenAI and Anthropic APIs
- Hugging Face
Delivery
- Docker
- GitHub Actions
- AWS
- pytest
- Ruff and mypy
Is this the right way to buy?
Hire this way when
- The work is data heavy and Python is where the tooling already is
- You need retrieval or model integration built to production standards
- A manual process runs on a schedule and keeps breaking
- A Django application needs an engineer who will still be here next year
Look elsewhere when
- The work is a high-concurrency API layer, where Node is usually the better fit
- You want research rather than delivery, which needs a different kind of person
- The requirement is a website, where Python is the wrong tool entirely
- Nobody can tell us what the data means, because that blocks everything
How hiring runs
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Tell us the gap
What the team is building, what is missing, and how long you expect to need it. A rough answer is enough to start.
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Profiles within a week
You get a shortlist of engineers who are actually free, with the work they have shipped here. Not a database of people we would have to recruit first.
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You interview them
Technical interview, pair programming, take-home, whatever your normal process is. You decide, not us. Nobody joins your team without your yes.
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They start inside your process
Your repository, your board, your standups, your review standards. We do not run a parallel process alongside yours.
Agreed before anyone starts
Written into the contract rather than promised on a call. These are the terms people forget to ask about until they need them.
You interview and approve every engineer before they start
One month notice either way, so neither side is trapped
A replacement at our cost if someone is not working out in the first month
Code, accounts and credentials are yours from the first commit
An NDA before any of your systems are discussed, not after
No recruitment fee if you later hire someone permanently
Questions we get asked
Django or FastAPI?
Django when you want batteries included: admin, auth and migrations out of the box. FastAPI when it is a service layer and you want speed and typed schemas. We will recommend one and explain why.
Do they do machine learning?
They integrate and operationalise models. Training a novel model from scratch is a research role and we would tell you to hire for that separately rather than pretending otherwise.
Can they work with our existing data warehouse?
Yes. Reading someone else's schema and its quirks is a normal part of this work. Expect questions about what fields actually mean before anything is written.
How do you handle model costs?
Caching, smaller models where they suffice, and logging of token spend from day one. Cost is a design constraint, not something to discover on the first invoice.
Tell us what your team is missing.
Send the role, the stack and how long you expect to need it. You get profiles of engineers who are actually free, not a sales call.