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Experience
Some experience
About the role
You will own the full lifecycle of internal AI products at a global investment company, from framing problems and building business cases through to launch, adoption and ongoing refinement. The role sits where investment workflows, agentic AI platform capabilities and user experience meet, so you will define what gets built, why it matters and how success is tracked. It suits a technically engaged product leader who wants to stay close to delivery teams rather than hand requirements to engineering.
What you'll do
Own the end-to-end lifecycle of assigned AI products, including discovery, opportunity sizing, solution design, prioritisation, launch, adoption tracking and iteration, with accountability for measurable business results.
Set product vision and strategy for AI use cases across investment, portfolio monitoring, research and operations workflows, turning senior stakeholder intent into sequenced roadmaps.
Make design decisions on AI systems, such as choosing between RAG, fine-tuning or prompt engineering, structuring agent workflows for reliability, adding human-in-the-loop checkpoints and designing evaluation frameworks.
Run product discovery by researching with investment professionals, observing real workflows, analysing pain points and turning findings into prioritised backlogs.
Own the evaluation strategy, defining success metrics, building evaluation datasets and maintaining benchmarks across model and prompt versions.
Translate implicit business needs from investment and operations teams into technically actionable AI product specifications covering data, model capability, integration points and error tolerances.
Maintain a product requirements framework for AI systems, including system prompts, agent behaviour specs, tool definitions, API contracts, evaluation rubrics, guardrails and feedback mechanisms.
Act as the main interface between business stakeholders and engineering, managing expectations, explaining probabilistic outputs to non-technical users and reporting performance transparently.
Build and maintain a business case framework linking product metrics like usage, accuracy and latency to business value metrics such as time saved, decision quality and risk reduced.
Lead cross-functional delivery squads of AI engineers, data engineers, UX designers and domain specialists, managing dependencies and escalating blockers with context and proposed solutions.
Partner with the AI Security & Governance Lead to embed governance and compliance throughout product development rather than at launch.
Work with the AI Fluency and Change Management team on adoption programmes, including user education, rollout strategy and feedback loops.
Engage the platform engineering team to feed product requirements into platform planning so common patterns become reusable components.
Design and deliver enablement programmes that help non-technical employees build low-code, no-code and AI-assisted solutions on approved enter
Requirements
4–8 years of product management experience
At least 2 years owning AI or ML products from problem framing to production deployment
Experience in a fast-moving AI-native organisation or a financial institution with a strong AI product function
Track record of shipping AI products that users adopt and rely on, with measurable impact
Technical depth to review prompt architecture, evaluate RAG pipeline design, understand model selection and contribute to evaluation frameworks
Domain experience in financial services such as investment research, portfolio management, operations or risk is an advantage
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