Senior Product Manager – Enterprise Search

Jobgether SRL; India; Hybrid; INR 3,000,000 - 6,000,000; Full time

Benefits offered by this job

Strategy ownership

High impact role

Cross-functional collaboration

AI/ML exposure

Flexible locations

Job summary

Jobgether SRL seeks a Senior Product Manager – Enterprise Search in India to own the end‑to‑end search journey across an AI‑powered employee experience platform.

You will lead ingestion, indexing, embedding pipelines, and retrieval strategies, collaborating with AI, data science, and engineering teams to deliver measurable customer outcomes.

Qualifications

• 5+ years in enterprise SaaS product management, with 2+ years in search/AI/ML products.
• Deep understanding of modern search stacks and data pipelines.
• Strong knowledge of LLMs, RAG, context assembly, and grounding.
• Experience with evaluation frameworks for search and RAG systems.
• Knowledge of enterprise security concepts in multi-tenant SaaS.
• Experience in SaaS environments—knowledge retrieval or enterprise software.
• Familiarity with vector search and hybrid retrieval architectures.
• Ability to translate complex tech into clear priorities and outcomes.

Responsibilities

• Own vision, strategy, and roadmap for enterprise search across AI and platform search.
• Define requirements, user stories, success metrics, and priorities for relevance and usability.
• Lead ingestion, chunking, indexing, and embedding pipelines with AI teams.
• Drive decisions on lexical, semantic, vector, and hybrid retrieval; consider latency and cost.
• Define how retrieved content feeds AI responses with reliable grounding and citations.
• Create evaluation frameworks using offline/online tests and golden queries.
• Monitor metrics: relevance, latency, adoption, reliability, cost, business impact.
• Ensure enterprise-grade security: permissions, tenant isolation, data governance.
• Collaborate across AI, search, data science, design, and product teams.
• Support customer-facing teams with expert knowledge on enterprise search.

Skills

Product management

Search and IR

AI/ML concepts

RAG knowledge

Stakeholder collaboration

Roadmap development

Security and governance

Latency and scalability

Education

CS/Engineering/Data Science degree

Tools

Elasticsearch

OpenSearch

Solr

Lucene

Pinecone

Weaviate

pgvector

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Product Manager – Enterprise Search based in India. This role offers the opportunity to define and lead the strategy for enterprise search across an AI-powered employee experience platform. You will own the end-to-end search journey, from content ingestion and indexing through retrieval, ranking, and AI-generated answers. Your work will directly influence how employees discover trusted, relevant, and secure knowledge across an enterprise. You will partner closely with AI, search, ML, data science, design, and cross-functional teams to turn advanced search technologies into measurable customer outcomes. The role combines deep technical fluency with product strategy, requiring strong judgment around relevance, latency, security, scalability, and cost. It is an ideal opportunity for a product leader passionate about search, information retrieval, RAG, and AI-native workplace experiences.

Accountabilities

• 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.
• Serve as the internal subject‑matter expert for enterprise search and support customer‑facing teams such as Customer Success, Sales, Support, and Marketing with search‑related expertise.
• Establish feedback loops and continuous improvement processes that use customer behavior, evaluation results, and production performance to improve search and RAG quality.
• Stay current with advances in enterprise search, information retrieval, generative AI, and AI‑native workplace experiences.

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, particularly within SaaS, collaboration, productivity, or enterprise software environments.
• 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, with the ability to translate complex technical concepts into clear priorities and measurable outcomes.
• Excellent communication and collaboration skills, with the ability to work effectively with both highly technical teams and non‑technical stakeholders.
• Strong customer orientation and an understanding of how enterprise users discover, consume, and trust organizational knowledge.
• Ability to operate effectively in a fast‑moving environment while balancing technical complexity, customer needs, security requirements, and business priorities.
• Passion for AI‑native products and improving how people access trustworthy enterprise knowledge through intelligent assistants and search experiences.

Benefits

• Opportunity to own the strategy and roadmap for a critical enterprise search and AI product area.
• High‑impact role at the intersection of enterprise SaaS, search, information retrieval, and generative AI.
• Direct collaboration with AI engineers, search and ML specialists, data scientists, designers, and cross‑functional product teams.
• Opportunity to shape AI‑powered employee experiences and enterprise knowledge discovery.
• Exposure to advanced technologies including vector search, hybrid retrieval, embeddings, LLMs, and RAG.
• Significant ownership over product strategy, evaluation frameworks, and measurable customer outcomes.
• Flexible working model with the opportunity to work from the Bengaluru or Gurugram locations according to role requirements.
• Collaborative environment with opportunities to work across Product, Engineering, Customer Success, Sales, Support, and Marketing.
• Opportunity to influence how enterprise customers securely discover and interact with organizational knowledge.
• Career growth opportunities within a rapidly evolving AI and enterprise software environment.