Machine Learning Quality Engineer Job London area,Greater London England UK,Software Development

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
The scope of work 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
- Establishing evaluation, quality control, and governance mechanisms so MCP tools and Skills can be promoted through PTB/PTO and operated with confidence at scale
Quality Engineers - Responsibilities &

Skills:
- Define and implement evaluation frameworks for MCP tools and Skills covering correctness, safety, and regression impact
- Build and maintain automated test pipelines for agentic behaviours, including tool invocation and multi-step workflows
- Evaluate and mitigate agentic failure modes such as hallucination, tool misuse, invalid inputs, and latency amplification
- Produce testing evidence required for Permit to Build (PTB) and Permit to Operate (PTO)
- Partner with ML Engineers to embed testability and evaluation hooks into MCP servers and Skills
- Help define the long-term quality and governance model for federated MCP contributions across LSEG

Strong Python experience for test harnesses and automation
Experience with LLM and RAG evaluation frameworks or custom evaluation pipelines
Test automation expertise covering unit, integration, and regression testing
Understanding of agentic system risks and failure modes
Ability to assess solutions against governance, security, and audit expectations
Experience working in regulated or highly governed engineering environments