Machine Learning Engineer

Data Scientist / Machine Learning Scientist Location: London (Hybrid) Contract: Outside IR35 Rate: £500–£550 per day (depending on interview outcome) We’re looking for AI operators who ship — not experiment. This is an opportunity to join a major AI build focused on deploying real-world LLM and agentic systems at scale across both AI products and enterprise transformation initiatives. You’ll be working in a production-first environment where the emphasis is on building reliable, scalable AI systems that deliver measurable business impact. What You’ll Be Working On • Designing and building AI agents and agentic workflows powered by LLMs • Developing systems using RAG, reasoning, planning, memory, and tool orchestration • Building multi-step intelligent systems capable of real-world tool usage • Working with MCP-style architectures (or equivalent) to structure context and improve interoperability • Contributing to recommendation, classification, and forecasting systems using large-scale datasets • Automating business workflows and decision-making processes through AI-driven solutions What You’ll Be Doing • Owning projects end-to-end from concept through to production deployment and iteration • Building and deploying AI agents that operate reliably in production environments • Integrating AI systems into APIs, products, and operational workflows • Collaborating closely with engineering teams to ensure scalability, observability, and maintainability • Making pragmatic decisions balancing model performance, latency, and cost efficiency Core Requirements • Strong Python skills with experience writing production-grade code • Proven experience deploying LLM-powered systems into production environments • Hands-on experience with LangChain, LangGraph, or equivalent orchestration frameworks • Experience building AI agents and agentic workflows with tool usage and multi-step reasoning • Strong understanding and implementation experience of RAG systems • Familiarity with MCP/FastMCP/FastAPI or similar orchestration patterns • Strong understanding of LLM trade-offs including hallucination mitigation, latency, and cost optimisation • Experience deploying AI systems in cloud environments such as AWS, GCP, or Azure • Working knowledge of SQL/data manipulation (Working knowledge of SQL or data manipulation is expected, but it is not a primary focus for this role.) Strong signals include: • Experience working on SaaS or B2B AI products or delivering AI-driven transformation within an organisation. • A background in high-growth or scaling environments, where speed and pragmatism are critical. • Clear evidence of systems that are actively used and delivering value, rather than experimental work. Ideal Background • Masters degree or higher in Computer Science, Mathematics, Engineering, or a related technical field • Experience building and iterating on AI systems delivering measurable value • Strong ownership mindset and ability to operate in fast-moving environments • Product-focused approach with a bias toward delivering impact Why This Role • Work on live AI systems used at scale • Join a well-supported AI engineering environment • High ownership and visibility across products and operations • Opportunity to shape enterprise AI adoption in a meaningful way