Machine Learning Engineer

Machine Learning Engineer

1. Role overview

  • You own the parts of a system that read, decide and act: retrieval over a client's own records, the rules around a model, and the evaluation that says whether it is safe to run.

  • This is engineering, not research. What you build is measured on whether it carries the work in production, on the client's data, under their controls.

  • You work beside the engineer deployed to the client, and you stay with the system after it goes live.

2. Key Responsibilities

  • Build retrieval and grounding over a client's own records, read in place

  • Design the evaluation set that decides whether a system is fit to run

  • Keep behaviour predictable: the same input, the same decision, and a record of both

  • Instrument every action so it can be traced back to what the system read

  • Tune cost and latency so a system is affordable to run every day

  • Adapt models to a client's own language, forms and edge cases

  • Write down what was tried, so the next engagement does not repeat it

3. Qualifications

  • Strong Python, and at least one system you took from a notebook into production.

  • Experience with retrieval and embeddings, and with the failure modes of both.

  • You design evaluations that can prove you wrong, not ones that flatter the model.

  • Comfortable with Docker, CI and at least one cloud, including inside someone else's account.

  • Experience with data that is not allowed to leave the client's environment.

  • You can explain the trade-off between accuracy, cost and latency to a non-specialist.

  • Curious about the open-source ecosystem without needing the newest thing in it.

  • You would rather be measured on production behaviour than on a benchmark.

4. The work

  • Understand the client's process before choosing the technology.

  • Build systems for real operational work.

  • Work with the people who know the process.

  • Learn from the behaviour of systems in production.

  • Choose tools to fit the client's platforms and controls.

  • Document decisions so other engineers can follow the work.

  • Connect engineering decisions to the work the system must carry.

  • Keep the client's people involved as the system evolves.

Type

Department

Engineering

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Lyrion

AI engineering firm

Technology Should Solve Real Problems.

Every solution we build is designed around business value, not unnecessary complexity.

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Lyrion

AI engineering firm

Technology Should Solve Real Problems.

Every solution we build is designed around business value, not unnecessary complexity.

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Business systems connected

Get In Touch:

By submitting, you agree to our Terms and Privacy Policy.

Let’s

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The Possibility

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Lyrion

AI engineering firm

Technology Should Solve Real Problems.

Every solution we build is designed around business value, not unnecessary complexity.

0+

Business systems connected

0%

Actions logged and reviewable

0%

Less Manual Work

Get In Touch:

By submitting, you agree to our Terms and Privacy Policy.

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