Technical Product Manager (Data Search & AI squad)
Posted 15 days ago
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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.
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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.
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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.
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Own evaluation: design the eval sets and metrics that determine whether the search is actually good, and use them to drive iteration.
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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.
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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.
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Partner closely with the Data Product and Platform squads on what data is being indexed and what new datasets unlock new search capabilities.
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Work with stakeholders outside Product - Sales, Customer Success, Engineering leadership - to keep the squad connected to commercial and operational reality.
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At least 3 years of proven experience as a Product Manager or Technical Product Manager on a product where AI, ML, or search is core - not a side feature.
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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.
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Strong product thinking: discovery, customer research, prioritisation, owning outcomes.
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Comfort making product tradeoffs in AI: precision vs recall, latency vs quality, cost vs capability, when to use deterministic logic vs models.
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Experience defining and using evaluation frameworks for AI or search products - knowing how to tell whether your system is actually getting better.
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Experience working with cross-functional teams and stakeholders outside product.
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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.
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Key team members

Giedrius Stalioraitis

Aleksandr Goriunov

Dainora Tverijonė

Virginijus Magelinskas
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