Machine Learning Engineer in East Kilbride

Machine Learning Engineer
Location: London
Salary: £95,000-120,000
Contract: Permanent
Working pattern: On site, 3 days a week
A new software venture built inside an established global provider of analytical and research services to the private capital industry is hiring Machine Learning Engineers to build the models, agents and evaluation systems that investment professionals rely on to analyse data, work with documents and automate research. You'll join the UK core team of around thirty people, working in Python on production ML and LLM systems that sit behind data-dense enterprise products.
THE ROLE
You'll report to the Head of Engineering and own the machine learning layer of the platform, from the first prototype through to a monitored production service. The work combines applied LLM engineering with the rigour that high-stakes financial workflows demand.
The hard part is reliability and measurement: a model that is right most of the time is not enough when analysts act on the output, so every system needs to be evaluated, observable and safe to fail.
Part of an engineering cohort being hired across machine learning, front-end and back-end.
Based with the UK core team of around thirty people in Kings Cross, alongside colleagues in London and India.
WHAT YOU'LL BE DOING
Your remit spans the models and agents, the data and retrieval layer, the evaluation framework and the production platform.
Design, build and ship LLM-powered features and multi-step agent workflows in Python, from prototype to production.
Build document intelligence pipelines that extract, structure and reason over long, messy financial documents such as filings, decks, models and data rooms.
Develop retrieval-augmented generation systems, including chunking, embeddings, vector search, re-ranking and citation, so that every answer can be traced to its source.
Create evaluation frameworks and datasets, with automated regression tests, human review loops and metrics for accuracy, that catch mistakes early and stop the same failures recurring.
Implement guardrails, confidence signalling and safe failure behaviour, and work with front-end colleagues to expose approvals, tool calls and partial progress to users.
Fine-tune, prompt-engineer and benchmark models, and make ...