Machine Learning Engineer

About the Role

We are looking for an experienced Machine Learning Engineer to design, develop and deploy production-ready AI and machine learning solutions.

This role will focus on building scalable Large Language Model (LLM) applications, AI agents and Retrieval-Augmented Generation (RAG) systems, with a strong emphasis on performance, reliability and continuous evaluation.

The ideal candidate will combine strong software engineering skills with hands-on experience delivering AI solutions in production environments. We are particularly interested in engineers who take an evaluation-led approach to development and actively use AI-assisted coding tools to improve efficiency and delivery.

Key Responsibilities

• Design, build and maintain production-grade machine learning and LLM applications.

• Develop AI agents and orchestration workflows using frameworks such as LangGraph, LangChain or similar technologies.

• Build and optimise RAG pipelines, including document processing, embeddings, vector databases and retrieval strategies.

• Implement graph-backed retrieval solutions and work with graph databases such as Neo4j.

• Develop evaluation frameworks to measure model performance, retrieval accuracy and overall system reliability.

• Collaborate with software engineers, data teams and business stakeholders to translate requirements into scalable AI solutions.

• Optimise LLM applications for performance, scalability, cost and maintainability.

• Integrate AI-assisted development tools into engineering workflows while maintaining strong coding and testing standards.

• Monitor, troubleshoot and continuously improve deployed ML and AI systems.

• Contribute to technical architecture decisions, development standards and AI engineering best practices.

Key Requirements

• 5+ years of software engineering experience, with strong Python development skills.

• 2+ years of hands-on experience building and deploying LLM or machine learning systems in production.

• Strong experience with AI agent and orchestration frameworks such as LangGraph, LangChain or comparable technologies.

• Proven experience developing production RAG systems, including embeddings, vector databases, chunking strategies and retrieval evaluation.

• Experience with graph-backed retrieval or a strong understanding of graph data modelling, with the ability to work with Neo4j and Cypher.

• Demonstrable experience with evaluation-led development, including designing and maintaining evaluation frameworks and test harnesses.

• Regular use of AI coding tools such as Cursor, Claude Code or similar, with an understanding of their strengths and limitations.

• Strong understanding of software engineering best practices, testing, debugging and production deployment.

• Excellent analytical, problem-solving and communication skills.

Desirable Experience

• Production experience with vector databases such as Qdrant, Weaviate or pgvector.

• Experience with document ingestion and processing tools such as Docling or Unstructured.

• Knowledge of Model Context Protocol (MCP) and experience developing MCP servers.

• Familiarity with LLM observability and evaluation tools such as LangSmith, Langfuse or Ragas.

• Experience with model fine-tuning, embedding-model selection or deploying open-weight models.

• Understanding of financial services data and document types, including regulatory filings, fund documentation and investment research.

• Experience optimising AI systems for latency, cost, accuracy and reliability.

Education

• Bachelor's degree in Computer Science, Engineering or a related technical discipline.

• Equivalent practical experience and a strong technical background will also be considered.

Interested?

If you have a strong software engineering background and proven experience building production-ready LLM and machine learning solutions, we'd love to hear from you.

Apply today or get in touch to find out more.