Senior Machine Learning Engineer
Posted Updated
- 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
- 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
- Design and build MCP servers and tools that expose enterprise systems and workflows to AI agents
- Implement Skills that orchestrate tools, data, and reasoning into repeatable, governed workflows
- Contribute to the LSEG AI Assist agentic harness, including planning, tool‑calling, and orchestration logic
- Build secure API wrappers where backend systems lack suitable authentication or entitlement models
- Work closely with product teams in a “build for” capacity, transferring knowledge and establishing reusable patterns
- Shape the developer experience for MCP and Skills, including templates, contribution guidance, and standards
- Collaborate with Quality Engineers and SREs to ensure solutions meet quality, governance, and operational readiness expectations
- 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