Machine Learning Engineer

Location: King's Cross, London (in-office) | Experience: 5+ years | Openings: 4

About my client

My client is building an AI work-execution platform for alternative investment firms and their portfolio companies, securely bringing together proprietary data, external sources and workflows to deliver faster, more contextual analysis and better decisions. Backed by a global group of 1,800+ professionals with deep expertise in investment diligence, research, valuation and fund administration, the UK core team is small, senior and high-ownership, with direct access to the Executive Committee.

The role

You will design, build and evaluate the LLM-powered systems at the heart of the platform, working in Python/FastAPI with LangGraph and LangChain across leading model providers. Token cost, latency and auditability are first-class engineering constraints. Senior engineers own major capabilities. Lead engineers also set technical direction and mentor.

Key responsibilities

• Build multi-step agentic workflows (LangGraph / LangChain) with tool-calling, checkpoints and human-in-the-loop patterns
• Integrate Anthropic, OpenAI and Amazon Bedrock models, owning routing, fallback and context and cost management
• Build structured-output systems and MCP tool servers for reasoning over investment documents
• Own production RAG pipelines (Qdrant, reranking, hybrid search) and extend GraphRAG on Neo4j
• Run evaluations (golden datasets, LLM-as-judge, regression gates) and instrument every model call for latency, cost and quality
• Apply responsible-AI guardrails in a UK/GDPR context, shipping on FastAPI, PostgreSQL and AWS with CI/CD
• 5+ years of software engineering with strong Python, including 2+ years running LLM or ML systems in production
• Deep hands-on experience with an agent framework (LangGraph, LangChain or similar) beyond prototypes
• Production RAG experience: embeddings, vector databases, chunking and retrieval evaluation
• Graph-retrieval experience, or the data-modelling fundamentals to pick up Neo4j and Cypher quickly
• Evaluation-led development: you can describe the eval harness of your last system from memory
• Daily use of AI coding tools (Cursor, Claude Code or similar), with a considered view of their strengths and limits

Nice to have:

Qdrant, Weaviate or pgvector in production; MCP servers; LangSmith / LangFuse / Ragas; fine-tuning or open-weight deployment; financial-services documents.

Education:

Bachelor's degree in engineering, computer science or a related field, or equivalent experience.

What's on offer

• Close collaboration with an Executive Committee with 20+ years' experience serving 400+ leading alternative investment clients
• Competitive compensation
• The chance to build something impactful for leading financial services firms
• A supportive, collaborative environment in a central London office