At a Glance
- Tasks: Lead the strategy and design of AI telemetry systems for monitoring and analysis.
- Company: Join a leading financial services company focused on AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on AI governance and responsible practices.
- Why this job: Shape the future of AI observability and make a real impact in the tech world.
- Qualifications: Experience in product management and strong analytical skills required.
The predicted salary is between 70000 - 90000 € per year.
Requirements
- 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
- (Desirable) Exposure to financial data, analytics systems and enterprise-scale data workflows
- (Desirable) Familiarity with BI tools, metrics stores, distributed tracing and monitoring stacks
- (Desirable) Understanding of cloud infrastructure, serverless runtimes, API gateways and model hosting architectures
- (Desirable) Ability to drive cross-functional alignment and establish group-wide standards
What the job involves
As part of the LSEG AI, we are hiring a Senior Product Manager – AI Observability to 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 & risk
- Model performance tracking
- Cost efficiency
- User experience optimisation
- Operational reliability
- Auditability
- And the long-term evolution of our AI platform
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 required for:
- Quality & reliability
- Latency & throughput
- Cost tracking & optimisation
- MCP usage patterns
- User workflow insights
- Failure pattern detection
- 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 behavior 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 (previous role), 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 behavior and user impact
- Ensure telemetry systems support auditability, compliance and explainability requirements
Measures of Success:
- Platform Reliability & Performance: Reduction in AI system error rates, latency spikes and operational incidents; Stability and transparency of MCP usage across product lines; Quality and coverage of telemetry signals across all AI-enabled products
- Insight Generation & Adoption: Adoption of telemetry dashboards and self-service tools across divisions; Number of identified and resolved model or MCP reliability issues driven by telemetry; Level of insight generated to influence product prioritisation and AI model choices
- Operational Efficiency & Cost: Improved cost transparency and cost-saving impact via telemetry-driven optimisation; Reduction in unnecessary model invocation or inefficient tool usage
- Governance & Compliance: Contribution to Responsible AI frameworks through robust, auditable telemetry; Improved explainability and traceability of AI features for internal/external stakeholders
Senior Product Manager (AI Observability) in London employer: Deepstreamtech
At LSEG, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a Senior Product Manager in AI Observability, you will have the opportunity to lead cutting-edge projects while benefiting from our commitment to employee growth through continuous learning and development programmes. Located in a vibrant city, our team enjoys a supportive environment that values diversity and encourages meaningful contributions to the future of AI technology.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Product Manager (AI Observability) in London
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio or a personal project that highlights your experience with telemetry systems and AI observability. It’s a great way to stand out from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions related to product management and AI. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Senior Product Manager (AI Observability) in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience in product management, especially in observability and telemetry. We want to see how your skills align with the role, so don’t hold back on showcasing relevant projects!
Showcase Your Analytical Skills:Since this role requires strong analytical abilities, include examples of how you've derived insights from large-scale system telemetry in your previous roles. We love seeing concrete results, so share any metrics or outcomes that demonstrate your impact.
Highlight Cross-Functional Collaboration:This position involves working closely with various teams, so be sure to mention any experience you have in collaborating with engineering, data science, or compliance stakeholders. We’re looking for team players who can drive alignment across functions!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Deepstreamtech
✨Know Your Tech Inside Out
Make sure you have a solid grasp of observability, telemetry, and AI inference workflows. Brush up on your knowledge of LLMs, embeddings, and the tools used in monitoring. Being able to discuss these topics confidently will show that you're not just familiar with the concepts but can also apply them.
✨Showcase Your Analytical Skills
Prepare to demonstrate your analytical prowess by discussing how you've derived insights from large-scale system telemetry in the past. Bring examples of how your analysis led to improvements in product performance or user experience. This will highlight your ability to turn data into actionable strategies.
✨Cross-Functional Collaboration is Key
Be ready to talk about your experience working with various stakeholders, like engineering and data science teams. Share specific instances where you drove alignment across functions to achieve a common goal. This will illustrate your ability to foster collaboration and establish standards.
✨Understand the Business Impact
Familiarise yourself with the financial data and analytics systems relevant to the role. Be prepared to discuss how your work in AI observability can drive cost efficiency and improve ROI. Showing that you understand the business side of things will set you apart as a candidate who can bridge the gap between tech and strategy.
