Technical Architect

London Stock Exchange; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

London Stock Exchange is expanding its Corporate Engineering AI team to scale safe, high‑quality AI capabilities across the enterprise. The role focuses on governing architecture artefacts, securing design approvals, and aligning with security, data and resilience standards.

You will translate complex AI/ML designs into clear documentation, engage stakeholders across Cyber, Risk, Legal and Compliance, and collaborate with product engineering to deliver scalable, governable AI platforms.

Qualifications

• Owns architecture artefacts in a governed enterprise.
• Understands architecture governance and stakeholder engagement.
• Translates complex AI/ML designs into clear, comprehensive docs.
• Knowledge of enterprise architecture: security, cloud, data, resilience.
• Familiar with LLM, RAG, and agentic system architectures and risks.
• Strong communication across technical and non-technical audiences.

Responsibilities

• Own and maintain architecture artefacts with governance alignment.
• Drive architecture governance and secure design approvals.
• Engage Cyber, Risk, Legal, Privacy and Compliance stakeholders.
• Translate AI/ML designs into accessible documentation for teams.
• Ensure designs meet security, resilience, observability and compliance.

Skills

Architecture artefacts

Governance processes

Stakeholder engagement

LLM/RAG architectures

Enterprise security/resilience

Documentation clarity

Job description

Overview

• 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.

Program Scope

• 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.
• Own and maintain SDD/ADD design documentation as governed, up‐to‐date artefacts.
• Drive designs through architecture governance, securing Dev/Test and Production approvals.
• Engage Cyber, Risk, Legal, Privacy, and Compliance stakeholders to validate and approve designs.
• Represent solutions in governance forums, clearly explaining architecture, risks, and controls.
• Ensure solutions meet security, resilience, scalability, observability, and compliance requirements.
• Align implementation with approved design, maintaining traceability and documentation integrity.
• Partner with ML, Quality, and SRE teams to ensure designs are deliverable and production-ready.

Qualifications

• Experience owning architecture artefacts in a governed enterprise.
• Strong understanding of architecture governance processes and stakeholder engagement.
• Ability to translate complex AI/ML designs into clear documentation.
• Knowledge of enterprise architecture principles (security, cloud, data, resilience).
• Familiarity with LLM, RAG, and agentic system architectures and risks.
• Strong stakeholder management and communication skills across technical and non-technical audiences.