Location
Paternoster Square, 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. The scope 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 Permit to Build (PTB) and Permit to Operate (PTO) and operated with confidence at scale. The programme follows a “build for” model today, 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 Machine Learning Quality Engineer, you will play a key role in defining and implementing evaluation frameworks for MCP tools and Skills, building and maintaining automated test pipelines for agentic behaviours, and mitigating agentic failure modes. You will work closely with ML Engineers to embed testability and evaluation hooks into MCP servers and Skills and help define the long-term quality and governance model for federated MCP contributions across LSEG.

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, value diversity and individuality, and offer a range of tailored benefits and support including healthcare, retirement planning, paid volunteering days, and wellbeing initiatives.

Experience
- 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

About you
You are a detail-oriented and proactive engineer with a passion for quality assurance in AI systems. You understand the complexities and risks of agentic AI and are committed to delivering safe, reliable, and high-quality AI capabilities at scale. You thrive in collaborative environments and are eager to contribute to defining long-term governance and quality models.

Qualifications
Senior Associate level experience in machine learning quality engineering or related fields is expected. Relevant technical qualifications or certifications in software testing, AI, or machine learning are advantageous but not mandatory.