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Centricone Technologies
Staff augmentation

Hire Python developers matched to the kind of Python you actually write

Backend service work, data engineering, and AI systems are three different jobs that share a language. We screen for the one you need rather than for the keyword.

Tell us the gapHow our staffing works

Screening

What we check that a keyword match does not

Every candidate sits a live assessment with an engineer who does this work. For this role, that conversation covers:

  • Which Python they actually do — services, pipelines, or modelling — probed rather than assumed
  • Type discipline and testing habits in a language that does not enforce either
  • Data handling: memory behaviour, batching, and where a pipeline will fall over
  • Dependency and environment management, which is where Python projects rot

What they do once they are in your team

01

FastAPI or Django services with typed, documented interfaces

02

Scheduled data pipelines with retries, lineage, and alerting

03

Retrieval, evaluation, and model-serving work for AI systems

04

Performance investigation where the interpreter genuinely is the constraint

Which level you actually need

Most briefs ask for senior by default. Sometimes that is right; often the work is better served by a mid-level engineer and a clearer specification.

Mid-level

Delivers within an existing service or pipeline, with review on data modelling and failure paths.

Senior

Designs pipeline or service architecture and owns its operational behaviour, including cost.

Lead

Sets standards across data or platform engineering and carries delivery accountability.

Questions about hiring Python developers

Not sure what you need? Describe the work and we’ll tell you which role fills it.

Not automatically. A strong backend Python engineer is not necessarily an ML or retrieval engineer — the overlap is the language, not the skill. Tell us which problem you have and we screen for that specifically.
It varies and we assess against your stack rather than a generic list. Data engineering tooling is specific enough that we would rather test on what you actually run.
A real distinction, and one we screen for. Plenty of capable analysts write excellent notebooks and struggle with production service code. If you need deployable systems, that is what the assessment covers.
Python spans a wide range of work and the rate follows it. A web backend engineer prices differently from someone who can productionise machine learning, because the second pool is much smaller. Being specific about which one you need is the single biggest lever you have on both cost and time to fill.
Both appear in the pool, and the assessment covers whichever matters to your role. The frameworks are close enough that a strong engineer moves between them quickly; we still screen on the one you actually run, because the ecosystem habits around ORMs, migrations, and async differ more than the syntax does.
That is a specific skill and we screen for it when the role calls for it. It is less about Python and more about testing, packaging, dependency pinning, and designing for reruns and failure — which is precisely the gap that leaves promising models stuck in notebooks.

Keep reading

Talent management

Engagement models side by side, the screening gates every candidate clears, and what happens after a placement starts.

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Python development services

Rather have us build it than staff it? This is what we deliver with Python.

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Other roles we staff