Machine Learning Engineer

Salary: £49,000 - 89,000 per year

Requirements:

• 5+ years of experience in machine learning or software engineering, including production ML experience and strong Python skills.
• Hands-on experience shipping LLM-based or NLP systems to real users, including RAG, agents and tool use, and the ability to explain design and trade-off decisions.
• A rigorous approach to model evaluation, including defining quality, building test sets and measuring results.
• Strong grounding in the ML stack, such as PyTorch, scikit-learn, Hugging Face and vector databases, alongside sound software engineering practices.
• Experience deploying and monitoring models on cloud infrastructure, ideally AWS, with Git, CI/CD and containerisation.
• Experience building SaaS or multi-tenant products, with an understanding of data security, access control and privacy.
• Daily use of AI coding tools such as Cursor or Claude Code, and a considered view of where they help.
• A degree in computer science, engineering, mathematics, statistics or a related field, or equivalent experience. A postgraduate degree is valued but not required.
• Nice to have: experience building agentic products, including orchestration, memory, tool calling and human-in-the-loop controls.
• Nice to have: experience with document understanding, OCR and table extraction for complex financial documents.
• Nice to have: experience fine-tuning or distilling language models and optimising inference.
• Nice to have: experience with financial, investment or other regulated-industry data.
• Nice to have: experience with LLM observability and evaluation tooling.

Responsibilities:

• Design, build and ship LLM-powered features and multi-step agent workflows in Python, from prototype through production.
• Build document intelligence pipelines to extract, structure and reason over complex financial documents, including filings, decks, models and data rooms.
• Develop retrieval-augmented generation systems using chunking, embeddings, vector search, re-ranking and citations so answers can be traced to their sources.
• Create evaluation frameworks and datasets, including automated regression tests, human review loops and accuracy metrics.
• Implement guardrails, confidence signalling and safe-failure behaviour, and work with front-end colleagues to show approvals, tool calls and partial progress to users.
• Fine-tune, prompt-engineer and benchmark models, choosing between hosted foundation models, open-weight models and classical ML based on cost, latency and quality.
• Build and operate ML services and APIs with authentication, role-based access and tenant isolation to keep client data separate.
• Own production MLOps, including experiment tracking, model and prompt versioning, monitoring, drift detection, cost and latency management, and incident response.
• Write unit, integration and evaluation tests, and ship through Jenkins and SonarQube pipelines to AWS.
• Work with back-end and front-end engineers and private-capital domain experts to turn analyst workflows into reliable products.

Technologies:

• AI
• AWS
• CI/CD
• Claude Code
• Cloud
• Cursor
• Fine-tuning
• Git
• Jenkins
• LLM
• Machine Learning
• MLOps
• PyTorch
• Python
• RAG
• Security

More:

We are hiring Machine Learning Engineers for a new software venture within an established global provider of analytical and research services to the private capital industry. Our platform supports investment professionals in analysing data, working with documents and automating research. You will join a UK core team of around thirty people, collaborating with colleagues in London and India, and report to the Head of Engineering. The role focuses on building the foundations of an enterprise-grade technology platform for high-stakes financial workflows, where reliability, measurement and safeguards are essential. The position is permanent, based in Kings Cross, London, with a salary of £95,000–£120,000 and an on-site working pattern of three days a week. Visa sponsorship is not available. The interview process is intended to finish within three weeks, and interview adjustments can be arranged on request.

last updated 40 week of 2026