AI Product Owner

The AI Product Owner will lead the end‑to‑end delivery of AI‑powered products and intelligent automation solutions across the insurance value chain, including underwriting, claims, customer service, operations, finance, and distribution. The role blends business product ownership, AI solution design, technology delivery, data‑driven decision making, and stakeholder management to identify opportunities, define product roadmaps, manage AI vendors, guide technical teams, and ensure successful implementation and adoption of AI solutions that deliver measurable business value.

• Define and own the AI product vision, roadmap, and backlog. • Identify use cases that deliver measurable business value. • Prioritise AI initiatives based on ROI, business impact, complexity, and risk. • Align AI initiatives with strategic business objectives. • Create business cases and value‑realisation frameworks. • Work closely with engineers, architects, and data scientists to design AI workflows, agent orchestration, prompt engineering, RAG architecture, human‑in‑the‑loop controls, explainability, and decision‑making workflows. • Provide technical leadership in LLMs, GenAI architecture, agentic AI frameworks, RAG architectures, prompt engineering, and vector databases. • Ensure adherence to responsible AI principles, model governance, AI explainability, regulatory compliance, data privacy, and AI risk management. • Establish processes for model monitoring, drift detection, prompt governance, auditability, and human review. • Drive business adoption, define KPIs, measure realised benefits, conduct training, and embed AI into operating processes.

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related discipline (Master’s preferred). • 8–12 years of experience in Product Management, Digital Transformation, Technology Delivery, or AI Solutions. • Minimum 5 years within the insurance sector. • Proven experience implementing enterprise AI solutions. • Strong technical skills in LLMs, prompt engineering, RAG, agentic AI concepts, AI orchestration frameworks, AI evaluation methodologies, conversational AI, and AI governance.

Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related discipline; Master’s degree preferred.