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Python
Django
PostgreSQL
Docker
AWSWhy hire Python Developers through us
- Vetted engineers — technical screening and a practical exercise before you see a profile
- You interview and choose; nobody joins without your approval
- Working hours overlap with your team, with clear daily communication
- NDA and full IP assignment to your company
- Monthly terms with a trial period and free replacement if the fit is wrong
Roles and seniority
| Level | What they do |
|---|---|
| Mid-level | Builds APIs, scripts and integrations |
| Senior | Designs systems, data models and pipelines |
| Lead | Owns architecture and mentors the team |
Skills matrix
Ways to engage
| Model | How it works | Best for |
|---|---|---|
| Full-time dedicated | One engineer, 40 hours a week on your work | Ongoing product development |
| Part-time | 20 hours a week | Maintenance and smaller roadmaps |
| Team | Several engineers with a lead | New products and workstreams |
Rates depend on seniority and engagement length. Share your requirements and you will receive a written rate card within one business day. See also staff augmentation and dedicated teams.
What a great Python developer looks like
Good Python developers write clear, idiomatic code that others can read, use type hints and tests to keep it reliable, and know the ecosystem well enough to choose the right framework for the job — Django for full applications, FastAPI for lean APIs, and data libraries for analysis and pipelines. They understand databases and performance, and for AI work they can move from a notebook experiment to a robust, monitored service. Senior engineers design for maintainability and can mentor others.
Questions we ask in technical interviews
Every candidate is assessed on fundamentals and on judgement. Typical topics include:
- When would you choose Django over FastAPI, or vice versa?
- How do you handle long-running tasks in a web application?
- How would you speed up a slow data-processing script?
- How do you manage dependencies and environments reproducibly?
- How would you deploy and monitor a machine learning model in production?
Typical projects for Python developers
- Building Django or FastAPI back ends for SaaS products
- Creating data pipelines that feed dashboards and reports
- Automating manual processes with scheduled jobs and integrations
- Building LLM-powered assistants and document processing
- Migrating legacy Python 2 code to modern Python
We match engineers by domain as well as language — web, data or AI — because the skills differ more than the shared syntax suggests.
Which kind of Python developer do you need?
“Python developer” covers very different jobs. Being specific about the work saves time for everyone:
| Focus | Typical work | Key skills |
|---|---|---|
| Web back end | APIs, admin panels, business logic | Django or FastAPI, SQL, authentication, testing |
| Data engineering | Pipelines, ETL, data warehouses | SQL, Airflow or Dagster, dbt, cloud data services |
| Machine learning | Models, training, evaluation, deployment | scikit-learn, PyTorch, MLOps, statistics |
| AI applications | LLM features, RAG, agents | LLM APIs, vector databases, evaluation, prompt design |
| Automation & scripting | Integrations, scheduled jobs, internal tools | APIs, scheduling, error handling, logging |
For data and AI roles, see also data engineers and AI/ML developers.
Red flags in Python candidates
- No type hints or tests in any recent code they can show
- Manages dependencies by installing packages globally, with no lock file
- Uses notebooks for everything, including code that should run in production
- Writes database queries in loops without noticing the performance cost
- Cannot explain how they would deploy, monitor and update their code
- For AI roles: cannot describe how they would measure whether a model or prompt is good enough
A sample Python developer brief
- The problem — for example, an API for a mobile app, a data pipeline or an AI feature
- Current stack — framework, Python version, database, cloud provider
- Data — sources, volumes and any sensitive information involved
- First deliverables — what success looks like in the first six to eight weeks
- Team and hours — who they report to, overlap hours and seniority
Tooling our Python developers use
From prototype to production
Python makes it easy to build a working prototype in days, which is a strength — and a trap. Code that works in a notebook or on one laptop often fails in production because of missing error handling, unpinned dependencies or memory limits. Our Python developers plan the path from prototype to production early: they move logic out of notebooks into tested modules, pin dependencies, add configuration through environment variables, containerise the service and set up logging and monitoring. For AI features they also add evaluation sets and cost tracking, so you know how well the feature works and what it costs to run.
If you need a complete team rather than individual developers, see Python development services.
Performance and cost tips for Python services
- Profile before optimising — the slow part is usually a database query or a network call
- Use async frameworks such as FastAPI for many concurrent I/O-bound requests
- Move heavy work to background workers with Celery, RQ or cloud queues
- Cache expensive results and LLM responses where it is safe to do so
- Choose vectorised libraries such as NumPy or Polars for large data operations
Before a Python developer starts, we confirm the Python version, packaging tool and deployment target, so their first pull request matches how your team already ships code.
Onboarding timeline
-
RequestDay 1
You share the role, skills and start date.
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ProfilesWithin 48 hours
Shortlisted candidates with relevant experience.
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InterviewsDays 3–5
You interview and select.
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StartWeek 2
Access, onboarding and first tasks in your tools.
Prefer a managed project instead of hiring? See our services.
Need a different skill set? See every role on Hire Software Developers.