Senior Product Manager – Enterprise Search
New
J
JobgetherEnterprise SaaS
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- Artificial IntelligenceElasticSearchMachine LearningProduct ManagementSaaSLLM
Requirements
- 5+ years of enterprise SaaS product management experience, including at least 2 years focused on search, information retrieval, AI, or ML-powered products.
- Bachelor's or advanced degree in Computer Science, Engineering, Data Science, or a related technical discipline.
- Deep technical understanding of the modern search stack, including ingestion, chunking, indexing, embeddings, vector databases, and lexical, semantic, and hybrid retrieval.
- Strong understanding of LLM and RAG concepts, including context assembly, grounding, citations, chunk selection, and approaches for reducing hallucinations.
- Experience developing or managing evaluation frameworks for search or RAG systems and using metrics such as relevance, precision, recall, latency, adoption, and business impact to guide product decisions.
- Working knowledge of enterprise security concepts, including access controls, permission-aware retrieval, tenant isolation, and data governance in multi-tenant SaaS environments.
- Experience with enterprise search, knowledge retrieval, or RAG-powered products.
- Familiarity with search technologies such as Elasticsearch, OpenSearch, Solr, Lucene, Pinecone, Weaviate, pgvector, or comparable managed search and vector solutions.
- Experience designing relevance-tuning workflows, feedback loops, or evaluation systems that continuously improve search quality in production.
- Strong product strategy and roadmap development capabilities.
- Excellent communication and collaboration skills.
- Strong customer orientation and an understanding of how enterprise users discover, consume, and trust organizational knowledge.
Responsibilities
- Own the product vision, strategy, and roadmap for enterprise search across conversational AI experiences and traditional platform search.
- Define product requirements, user stories, success metrics, and priorities around search relevance, freshness, coverage, trust, and usability.
- Lead the strategy for structured and unstructured data ingestion, collaborating with AI and search engineering teams on parsing, chunking, indexing, and embedding pipelines.
- Drive product decisions across lexical, semantic, vector, and hybrid retrieval, including re-ranking, filtering, permission-aware retrieval, latency, scalability, and cost considerations.
- Define how retrieved content is used to generate reliable AI responses through RAG, including context assembly, chunk selection, citations, grounding, and hallucination mitigation.
- Establish evaluation frameworks for search and RAG quality, including golden query sets, offline and online evaluations, and LLM-based evaluation approaches.
- Define and monitor product metrics covering relevance, precision and recall, latency, adoption, reliability, cost, and business impact.
- Ensure enterprise-grade security throughout the search experience, including document-level permissions, access controls, tenant isolation, and data governance.
- Collaborate with AI engineers, search and ML engineers, data scientists, designers, and other stakeholders to translate complex technical capabilities into scalable product experiences.
- Establish feedback loops and continuous improvement processes that use customer behavior, evaluation results, and production performance to improve search and RAG quality.
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