Location
London

Hours
Full Time

Salary
Negotiable

About the Role
The Corporate Engineering AI (CE AI) team is the central enablement and platform delivery function for LSEG’s internal agentic AI ecosystem. The team’s mission is to scale safe, high-quality AI capabilities across the enterprise by providing shared platforms, patterns, governance, and delivery support. CE AI owns and operates core AI platforms including LSEG AI Assist, the Question Answering Service (QAS), and the Internal MCP Gateway. Rather than delivering individual business use cases end-to-end, the team enables product engineering groups across LSEG to expose knowledge, data, and actions to AI agents in a consistent, governed, and repeatable way.

The team operates a Central MCP Delivery model: building critical MCP tools and services “for” product teams where required, while simultaneously defining standards, patterns, and platform capabilities that allow teams to progressively move towards self-service contribution.

The LSEG AI Assist / Internal MCP Programme of Work delivers an LSEG-owned, production-grade agentic AI platform with MCP as its extensibility layer. This includes building and operating LSEG AI Assist, an in-house agentic experience capable of reasoning, planning, and tool-calling; operating QAS, the enterprise RAG and search layer used to ground agent responses in approved data sources; delivering a production Internal MCP Gateway providing discovery, security, policy enforcement, observability, and lifecycle management for MCP tools and Skills; designing and building MCP servers and Skills that expose internal and vendor systems safely to agents; and establishing evaluation, quality control, and governance mechanisms so MCP tools and Skills can be promoted through PTB/PTO and operated with confidence at scale.

The programme currently follows a “build for” model with a strong emphasis on defining the future product and platform experience, patterns, and contribution pathways that will enable federated scale over time.

As a Senior ML Engineer, you will play a key role in designing and building MCP servers and tools that expose enterprise systems and workflows to AI agents, implementing Skills that orchestrate tools, data, and reasoning into repeatable, governed workflows, contributing to the LSEG AI Assist agentic harness, and collaborating closely with product teams, Quality Engineers, and SREs to ensure solutions meet quality, governance, and operational readiness expectations.

Join us and be part of a team that values innovation, quality, and continuous improvement. LSEG is a leading global financial markets infrastructure and data provider, committed to driving financial stability, empowering economies, and enabling customers to create sustainable growth. We foster a collaborative and creative culture where new ideas are encouraged and diversity is valued. We are proud to be an equal opportunities employer and support sustainability initiatives and community engagement through the LSEG Foundation.

LSEG offers a range of tailored benefits including healthcare, retirement planning, paid volunteering days, and wellbeing initiatives.

Experience
- Strong Python development experience
- Hands-on experience with LLM and agent frameworks and agentic reasoning patterns
- Practical understanding of Model Context Protocol (MCP), including server and tool patterns
- FastAPI and REST API design and implementation experience
- Experience with prompt engineering and RAG-based architectures
- Containerisation and Kubernetes-based deployment experience
- Ability to work across platform, product, and governance boundaries in an enterprise environment

About you
You are a motivated and skilled engineer with a passion for AI and machine learning, capable of working collaboratively across teams to deliver scalable, secure, and governed AI solutions. You thrive in a dynamic enterprise environment and are committed to quality, innovation, and continuous improvement.

Qualifications
Relevant advanced degree or equivalent experience in computer science, machine learning, or a related field is desirable but not explicitly stated. Demonstrated expertise and practical experience with the required skills are essential.