Machine Learning Engineer

Data Scientist / Machine Learning Scientist

Location: London (Hybrid)

Contract: Outside IR35

Rate: £500–£550 per day (depending on interview outcome)

Qualifications, skills, and all relevant experience needed for this role can be found in the full description below.

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. xwzovoh
• 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