Vilnius
Data Team
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.
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.
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.
Živilė Repšytė
Recruiter
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Vast internal & external learning resources
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Tesonet network & knowledge sharing
Yearly workation
Year-round internal & Tesonet community events
Team building budget
Cyber City office perks
Hybrid work (3 office days/ 2 WFH) & WFA options
Latest tools, gadgets & tech stack
Extra days off
Special Tesonet product deals
Private health insurance
24/7 gym
Physical well-being specialists
Psychologist/psychotherapist sessions
Inclusive family-related time-off policy
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