Lead Machine Learning Engineer

London Stock Exchange; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

London Stock Exchange is seeking a highly skilled Corporate Engineering AI engineer to help scale safe, enterprise‑grade AI capabilities. You will work on core platforms like LSEG AI Assist, QAS, and MCP Gateway, enabling product teams to expose data, knowledge and actions to AI agents in a governed, reusable way.

You will design and operate MCP tools, build agentic platforms, and ensure robust governance, observability and security across MCP servers and Skills.

Qualifications

• Strong Python development experience.
• Hands‑on experience with LLM and agent frameworks and agentic reasoning patterns.
• Practical understanding of 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.

Responsibilities

• The Corporate Engineering AI team scales safe, high‑quality AI capabilities across the enterprise by providing shared platforms, governance, and delivery support.
• Owns and operates core AI platforms like LSEG AI Assist, QAS, and the Internal MCP Gateway, enabling product teams to expose knowledge, data, and actions to AI agents.
• Builds MCP tools and services for product teams while defining standards, patterns, and platform capabilities for self‑service contribution.
• Delivers production‑grade agentic AI platforms with MCP as the extensibility layer.
• Designs and operates MCP servers and Skills exposing internal/vendor systems safely to agents.
• Establishes evaluation, quality control, and governance so MCP tools can be promoted and operated at scale.

Skills

Python

LLM frameworks

Agent frameworks

MCP (Model Context Protocol)

FastAPI

REST API

Prompt engineering

RAG architectures

Kubernetes

Tools

Docker

CI/CD

Job description

Responsibilities

• The Corporate Engineering 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
• This programme delivers an LSEG‑owned, production‑grade agentic AI platform with MCP as its extensibility layer
• 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
• Establishing evaluation, quality control, and governance mechanisms so MCP tools and Skills can be promoted through PTB/PTO and operated with confidence at scale

Qualifications

• 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