Machine Learning Engineer

Salary: £100,000 - 100,000 per year

Requirements:

• At least 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 a working 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 on 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 to production.
• Build document intelligence pipelines to extract, structure and reason over long, complex financial documents, including filings, presentations, models and data rooms.
• Develop retrieval-augmented generation systems, including 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, to catch mistakes early and prevent recurring failures.
• 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, making informed choices 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.
• Own the machine learning layer of the platform, from initial prototypes through to monitored production services.

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 products use production ML and LLM systems to help investment professionals analyse data, work with documents and automate research. You will join an engineering cohort spanning machine learning, front-end and back-end development, reporting to the Head of Engineering and working with a UK core team of around thirty people, alongside colleagues in London and India. The goal is to build the foundations of an enterprise-grade technology platform for a multi-billion-dollar serviceable market. The work involves building autonomous systems for high-stakes live businesses, where reliability, evaluation, observability and safeguards are essential. The role is based in Kings Cross, London, with three days a week in the office. It is permanent, with a salary of £95,000–£120,000. Visa sponsorship is not available. The interview process is intended to be completed within three weeks, and interview adjustments can be arranged on request. In conversations, we invite candidates to discuss a production ML or LLM system they shipped, an evaluation approach they designed, and a production failure or regression they addressed.

last updated 40 week of 2026