Senior Machine Learning Engineer

London Stock Exchange; Greater London; Vor Ort; GBP 90.000 - 130.000; Vollzeit

Zusammenfassung

London Stock Exchange is seeking a seasoned AI engineer to help design and scale our internal agentic AI platform across the enterprise.

You will work on MCP tooling, Skills, and secure API wrappers, collaborating with product teams to deliver governance, observability, and self-service patterns.

Strong Python development, FastAPI, Kubernetes, and prompt engineering are required.

Qualifikationen

• 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.

Aufgaben

• Design and build MCP servers and tools that expose enterprise systems safely to 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.
• Work closely with product teams in a “build for” capacity, transferring knowledge and establishing reusable patterns.

Kenntnisse

Python development

LLM frameworks

Agent reasoning

FastAPI

Tools

Kubernetes

REST API design

Containerization

Jobbeschreibung

• 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 experienceHands‑on experience with LLM and agent frameworks and agentic reasoning patternsPractical understanding of Model Context Protocol (MCP), including server and tool patternsFastAPI and REST API design and implementation experienceExperience with prompt engineering and RAG‑based architecturesContainerisation and Kubernetes‑based deployment experienceAbility to work across platform, product, and governance boundaries in an enterprise environment