Location
Paternoster Square, London

Hours
Full Time

Salary
Negotiable

About the Role
As part of the LSEG AI team, you will own the strategy, design and rollout of telemetry systems that monitor, measure and analyse how AI models, MCPs, and AI-enabled features behave across all LSEG products. This role is responsible for defining how we collect, structure, analyse and act on AI-specific telemetry signals—including prompt patterns, model performance, MCP call usage, latency, error conditions, cost metrics, user behaviour signals, and system reliability. You will develop a telemetry foundation that supports AI governance and risk, model performance tracking, cost efficiency, user experience optimisation, operational reliability, auditability, and the long-term evolution of our AI platform.

Key Responsibilities & Accountabilities
Telemetry & Observability Strategy
Define the end-to-end telemetry vision and roadmap for LLMs, MCPs, vector stores, embeddings, inference layers and AI-powered user experiences. Establish a standardised telemetry schema for capturing prompts, tool calls, model responses, durations, errors, confidence signals, and quality indicators across business lines. Partner with platform engineering to ensure instrumentation is consistent, scalable and compliant.
Signal Design & Data Architecture
Identify key signals for quality and reliability, latency and throughput, cost tracking and optimisation, MCP usage patterns, user workflow insights, failure pattern detection, and guardrail and safety event monitoring. Define retention rules, PII considerations, anonymisation and usage policies in partnership with AI Governance and Compliance.
Platform & Tooling
Own requirements for dashboards, monitoring tools, model comparison views, anomaly detection alerts, and performance scorecards. Design systems that allow product and engineering teams to self-serve insights about AI model behaviour and MCP interactions. Partner with engineering to build pipelines that support real-time and batch analytics.
Cross-Functional Collaboration
Collaborate with product owners across LSEG to instrument their AI features consistently. Partner with AI Evaluation PM, Model Risk, GSSR, Legal and Compliance to align telemetry with governance frameworks. Work closely with Engineering and SRE teams to drive observability improvements and reliability engineering for AI systems.
Optimisation & Insights
Identify cost inefficiencies across model and MCP usage, and drive recommendations to improve ROI. Surface workflow-level insights on how customers use AI features—informing product roadmaps. Partner with product leaders across divisions to understand how telemetry can improve customer experience, reliability and performance.
Governance & Standards
Define and maintain AI telemetry standards and best practices across all LSEG divisions. Contribute to LSEG’s AI governance and Responsible AI initiatives by providing data-driven insights on model behaviour and user impact. Ensure telemetry systems support auditability, compliance and explainability requirements.

Experience
Proven experience in product management with a strong foundation in observability, telemetry, data platforms, monitoring, or SRE/DevOps-driven products. Understanding of LLMs, embeddings, vector search, MCP tools, and AI inference workflows. Deep familiarity with logging, tracing, metrics, and event-based telemetry systems. Ability to define data schemas, signal taxonomies, aggregation strategies and data contracts. Strong analytical skills and ability to derive insights from large-scale system telemetry. Experience working with senior engineering, data science, risk and governance stakeholders.

About you
Ability to drive cross-functional alignment and establish group-wide standards. Strong collaboration skills to work with product owners, engineering teams, and governance bodies. Analytical mindset with a focus on operational efficiency, cost optimisation and governance. Familiarity with financial data, analytics systems and enterprise-scale data workflows is preferred. Comfortable working in a dynamic, innovative environment with a commitment to responsible AI practices.

Qualifications
While specific qualifications are not stated, a background in product management, data science, engineering or related fields with relevant experience in AI telemetry, observability or data platforms is expected. Knowledge of BI tools, metrics stores, distributed tracing and monitoring stacks, cloud infrastructure, serverless runtimes, API gateways and model hosting architectures is advantageous.