Top Tech Jobs & Startup Jobs in London

Provide second-line risk coverage for Corporate Functions (Legal & Compliance), identify and monitor non-financial risks, drive ERM framework implementation, oversee controls and remediation, prepare risk reporting and Enterprise Investment Portfolio insight, deliver training, and support first-line Risk and Control Assessments and transformation programs.

The Senior Manager will oversee non-financial, financial, and strategic risks in the Data & Analytics division, supporting order to cash processes, building partnerships across teams, conducting risk assessments, and driving improvements in controls and governance.

Lead Financial Crime oversight for Data & Analytics entities, ensuring AML and sanctions compliance, performing risk and control assessments, advising senior management and regulators, monitoring MI/KRIs, updating policies, and promoting FC awareness across the Group.

Lead development of quantitative research, analytics and automation platforms for equity and multi-asset indices. Build AI-enabled workflows, support research and index methodology, resolve complex data and operational issues, improve CI/CD and cloud infrastructure, and partner with cross-functional teams for product delivery and client support.

Review and validate index constituent eligibility and data for FTSE Russell indices, report exceptions, recommend process improvements, support onboarding and methodology updates, and collaborate across teams to ensure accuracy and timeliness.

Lead first-line technology risk and operational resilience across DSM, FX, and Risk Intelligence. Own control frameworks, governance, incident remediation, third-party and cloud risk, regulatory responses, risk reporting, and build a high-performing technology risk team to embed resilience across engineering.

Lead ML Engineer to design and build MCP servers, Skills, and agentic orchestration for an enterprise agent platform. Implement APIs, secure wrappers, RAG and prompt-based flows, deploy on containerised/Kubernetes platforms, and shape developer patterns, governance, and operational readiness while partnering with product teams, SRE and QA.

Define, document, and govern architecture for enterprise agentic AI platforms (LLM, RAG, MCP). Drive designs through governance, engage risk/privacy/compliance, ensure security/resilience/observability, and partner with ML, Quality, and SRE to make solutions production-ready.

Define and implement evaluation frameworks and automated test pipelines for agentic AI (MCP tools and Skills). Test tool invocation, multi-step workflows, and mitigate failure modes (hallucination, misuse, latency). Produce evidence for PTB/PTO, partner with ML engineers to embed testability, and shape long-term quality and governance for federated MCP contributions.

Design, build, and operate agentic AI platform components (MCP servers, Skills, QAS). Implement agent orchestration, tool‑calling, secure API wrappers, and RAG-based integrations. Collaborate with product teams, QA, and SRE to ensure governed, production-ready deployment and developer experience for enterprise AI agents.