Technical Product Manager (Data Search & AI squad)

In this role, you’ll

• Own the product strategy and roadmap for Data Search & AI - what we build, why, and how it ladders up to the company's agentic search bet.
• Lead the productisation of semantic search and agentic search over our data: deciding what queries we support, what "good" answers look like, and how AI capabilities are exposed to customers.
• Shape the AI stack at a product level - embedding models, retrieval strategies, ranking, reranking, agent design - making the tradeoff calls between relevance, latency, cost, and quality.
• Own evaluation: design the eval sets and metrics that determine whether the search is actually good, and use them to drive iteration.
• Decide when models are good enough to ship and when they need more work - coverage vs precision tradeoffs, hallucination tolerance, when to fall back to deterministic approaches.
• Drive customer discovery on how customers actually want to query our data - what they ask, what frustrates them today, what an AI-native interface should let them do that a traditional API or UI cannot.
• Partner closely with the Data Product and Platform squads on what data is being indexed and what new datasets unlock new search capabilities.
• Work with stakeholders outside Product - Sales, Customer Success, Engineering leadership - to keep the squad connected to commercial and operational reality.

Your skills & experience

• Proven experience as a Product Manager or Technical Product Manager on a product where AI, ML, or search is core - not a side feature.
• Working understanding of modern AI: embeddings and vector search, retrieval-augmented generation, evaluation methods, and the basics of how LLMs and agents work in production. You don't need to be an ML engineer - you do need to be able to reason about model choices, evaluate quality, and have credible conversations with the engineers building the system.
• Strong product thinking: discovery, customer research, prioritisation, owning outcomes.
• Comfort making product tradeoffs in AI: precision vs recall, latency vs quality, cost vs capability, when to use deterministic logic vs models.
• Experience defining and using evaluation frameworks for AI or search products - knowing how to tell whether your system is actually getting better.
• Experience working with cross-functional teams and stakeholders outside product.
• Comfortable operating in ambiguity - both because this is a newly formed squad and because AI products move fast and the right answer next quarter is rarely the same as the right answer this quarter.