Machine Learning Engineer

Overview

As a Machine Learning Engineer, you will own the ML layer of an enterprise-grade platform, from prototype to production. You will collaborate with a UK-based core team to build LLM-powered features, document intelligence pipelines, and retrieval-augmented generation systems for financial workflows. You’ll design robust evaluation regimes, guardrails, and production ML services that are observable and safe to fail. This role combines applied NLP/LLM work with high-stakes finance, in a multi-tenant SaaS context, offering a chance to shape reliable analytic products.

Responsibilities

• Design, build and ship LLM-powered features and multi-step agent workflows in Python, from prototype to production
• Develop document intelligence pipelines to extract and reason over complex financial documents
• Create retrieval-augmented generation systems with embeddings, vector search, and citation tracking
• Build evaluation frameworks and datasets with automated tests and human review loops
• Implement guardrails and safe-failure behaviour; expose approvals and tool calls to users
• Fine-tune and benchmark models, balancing hosted/open-weight models and classical ML for cost/latency/accuracy
• Operate ML services and APIs with authentication and tenant isolation
• Own MLOps in production including experiment tracking, versioning, monitoring, drift detection, and incident response
• Write tests and ship through Jenkins and SonarQube to AWS
• Collaborate with back-end/front-end engineers and domain experts to translate analyst workflows into reliable products

Key requirements

• 5+ years in machine learning or software engineering with production ML experience
• Hands-on experience shipping LLM-based/NLP systems (RAG, agents, tool use) and ability to justify design decisions
• Rigorous evaluation approach with clear metrics and test sets
• Strong grounding in ML stack (PyTorch, scikit-learn, Hugging Face, vector databases)
• Experience deploying/monitoring models on cloud infrastructure (ideally AWS) with Git, CI/CD, and containers
• Experience building SaaS or multi-tenant products with data security and access control understanding
• Daily use of AI coding tools (e.g., Cursor, Claude Code) and ability to assess their utility
• Degree in computer science/engineering/mathematics/statistics or related field, or equivalent experience; postgraduate valued but not required
• Familiarity with production-grade software engineering practices and observability

• clear communication of design decisions
• ability to work with cross-functional teams
• detail-oriented with a rigorous quality mindset
• PyTorch
• scikit-learn
• Hugging Face

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