AI / ML Engineer
Take models from evaluation into production, with the confidence scoring, review paths and monitoring that let a client trust the output.
AI / ML Engineer
Take models from evaluation into production, with the confidence scoring, review paths and monitoring that let a client trust the output.
About the role
Our AI work runs inside client environments, often under residency and audit constraints. The interesting part of the job is not the model — it is making the output something a reviewer will sign their name against.
What you will do
- 01Build the evaluation set before building the model
- 02Develop, fine-tune and evaluate models against agreed measures
- 03Design confidence scoring and the human review path
- 04Deploy inside the client’s own tenancy with proper MLOps
- 05Instrument for drift and failure, and act on what it shows
- 06Write the model card: what it does, where it fails, who reviewed it
What we are looking for
- Python and the modern ML / LLM toolchain in real use
- Can design an evaluation that would catch your own mistakes
- Understands retrieval, prompting and fine-tuning trade-offs
- Takes governance and traceability seriously
Useful, not essential
- MLOps and model deployment at scale
- Document extraction or clinical/legal NLP
- Published work or open-source contributions
How hiring works
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First conversation
Thirty minutes on what you have built and what you want to work on next.
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Practical exercise
A short task drawn from the actual job — done in a session, not over a weekend.
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Meet your lead
A conversation with the person you would report to, then a decision within a week.
We reply to every application, and we never ask for unpaid work.
Apply for this role
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