Application engineering
We ask candidates to structure a service, model types and errors, reason about concurrency and identify the query or application code causing a performance issue.
- Python
- RESTful APIs
- Microservices
- SQL
- PostgreSQL
UK, Europe, USA
Python developer recruitment
Python developers assessed in the context of backend, AI or data work rather than by one generic checklist.
Instruct us
Technical screening by developers and CTOs is included.
Python recruitment matched to the work
We recruit Python developers across backend services, AI applications and data engineering. The technical screen is shaped around the role’s actual responsibilities, including APIs, model workflows, pipelines, databases, cloud services, testing and production operation.
When to hire Python developers: A Python backend or API team. An applied AI or LLM product. A data pipeline and processing environment.
Technologies, requirements and soft skills
Python alone does not define a role. We establish whether the priority is application engineering, AI, data processing or a combination.
AI
We test retrieval design, evaluation and model failure handling, including how candidates prevent low-quality output from silently reaching users.
- AI
- LLM
- RAG
- OpenAI Codex
- Claude Code
Data and cloud
We ask candidates to design an observable ingestion flow, handle duplicate or late events and explain how they would test and replay a failed pipeline.
- BigQuery
- Dataflow
- Pub/Sub
- GCP
- Google Cloud
- PowerBI
- Looker
Soft skills and working style
We look for Python engineers who explain their reasoning, favour readable and testable solutions and can move between experimentation and production discipline without treating either as an afterthought.
- Analytical thinking
- Readable engineering
- Experimental curiosity
- Pragmatism
- Collaborative problem-solving
How we screen Python developers
We evaluate Python fundamentals and then go deeper into the domain that matters for the role.
Python engineering
Code structure, typing, testing, performance awareness and maintainability.
Domain depth
Backend, AI or data-specific design decisions and failure modes.
Production ownership
Databases, APIs, cloud services, observability and debugging.
What every candidate needs to demonstrate
Tool names are not enough. Across every role, we look for technical depth, disciplined use of AI and an honest approach to solving unfamiliar problems.
Understands what happens behind the scenes
We keep asking why and how until we reach the underlying behaviour. Candidates should understand what their framework, runtime, database, browser, cloud service or design tool is doing for them, how they work behind the scenes (and why), and how they would investigate a failure without relying on copying fixes from ChatGPT.
Uses AI with engineering discipline
We expect candidates to use AI productively, but never as a substitute for judgement. They need to explain how they verify generated code, designs and tests, protect confidential data, catch unsafe assumptions and remain accountable for the result. We expect them to use MCPs, skills, and goals. So basically - we are looking for structured AI-assisted work, not vibe coding, vibe designing or vibe testing.
Combines initiative with honesty
Strong candidates have a can-do mindset and will investigate, experiment and learn when the answer is not obvious. At the same time, they are fair and transparent about what they know, what they have not done before and when they need support.
Looking to hire?
Give us a few details about the role and your hiring plans. We'll assess your needs and get back to you with a practical next step.
Frequently Asked Questions
We recruit Python developers for backend engineering, AI and LLM applications, and data engineering roles.
Yes. For relevant briefs, the technical interview can cover LLM integration, RAG workflows, evaluation, data and production reliability.
Yes. We tailor the search to the cloud services, data pipelines, APIs, deployment model and operational ownership involved. Previous requirements have included GCP, BigQuery, Dataflow and Pub/Sub.