> Markdown version of [/jobs/ext/2824513-senior-ml-engineer](https://www.wearedevelopers.com/jobs/ext/2824513-senior-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Engineer - **Company:** LSEG - **Location:** Greater London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Python (Programming Language), Enterprise Software Applications, Large Language Models, Multi-Agent Systems, Prompt Engineering, Backend, Fastapi, Build Management, Question Answering, Containerization, Kubernetes, Restful APIs - **Published:** September 10, 2026 - **Apply:** https://www.collegerecruiter.com/job/2840588592-senior-ml-engineer ## About the Role * 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. ## Description 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. LSEG AI Assist / Internal MCP Programme of Work 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. ## Related Videos - [Beyond Prompting: Building Scalable AI with Multi-Agent Systems and MCP](https://www.wearedevelopers.com/videos/1454-beyond-prompting-building-scalable-ai-with-multi-agent-systems-and-mcp) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) - [Building and Deploying Multi-Agent Systems with ADK and Vertex AI](https://www.wearedevelopers.com/videos/1918-building-and-deploying-multi-agent-systems-with-adk-and-vertex-ai) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 210: AI Agents Are Go! Is MCP Dead? LLMs Crack Anonymity](https://www.wearedevelopers.com/magazine/709-dev-digest-210-ai-agents-are-go-is-mcp-dead-llms-crack-anonymity) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)