Senior Product Manager – Enterprise Search

The Opportunity

We're looking for a highly skilled Senior Product Manager – Enterprise Search to join our Product Management team. In this role, you'll own the strategy, definition, and execution of enterprise search across Simpplr — powering both Magnus (our AI assistant) and the Simpplr platform. You'll shape how employees find the right knowledge, content, and answers across the entire enterprise, quickly, accurately, and securely.

Search is the connective tissue of the employee experience: it is what makes Magnus's answers trustworthy and what makes the Simpplr platform feel intelligent. You'll be responsible for the end-to-end search experience — from how content is ingested, chunked, and indexed, to how it is retrieved, ranked, and grounded into high-quality, permission-aware answers via retrieval-augmented generation (RAG).

You'll work closely with AI engineers, search/ML engineers, data scientists, designers, and cross-functional teams to deliver a search foundation that is fast, relevant, and enterprise-grade. This role requires strong technical fluency in modern search and AI concepts — indexing, chunking, embeddings, vector and hybrid retrieval, ranking, and RAG — along with the product judgment to translate them into measurable customer outcomes.

Your job responsibilities: what you will be doing

• Own the roadmap for enterprise search across Magnus and the Simpplr platform, defining a clear vision for relevance, freshness, coverage, and trust — including product requirements, user stories, and success metrics for conversational (Magnus) and traditional search experiences.
• Own the strategy for unstructured and structured data ingestion, partnering with search/ML and AI engineers on parsing, chunking, and embedding pipelines that improve search relevance and retrieval accuracy.
• Drive retrieval architecture decisions — lexical, semantic/vector, hybrid retrieval, re-ranking, filtering, and permission-aware retrieval — balancing relevance, latency, and cost.
• Define how retrieved content is grounded into answers through RAG — context assembly, chunk selection, citations, and hallucination mitigation.
• Establish evaluation frameworks for search and RAG quality (golden query sets, offline/online evals, LLM-as-a-judge) and standards for reliability, latency, monitoring, and cost optimization.
• Ensure end-to-end security — permission scoping, document-level access, tenant isolation — and serve as the enterprise search SME, partnering with CS, Sales, Support, and Marketing to drive adoption.

 

Your skillset: what makes you a great fit for the team

• Bachelor's/advanced degree in Computer Science, Engineering, Data Science, or related field, with 5+ years of enterprise SaaS product management, including 2+ years focused on search, information retrieval, or AI/ML products.
• Deep technical fluency across the search stack: ingestion, chunking, indexing, embeddings, vector databases, and lexical/semantic/hybrid retrieval trade-offs.
• Solid grasp of LLM and RAG mechanics — context assembly, grounding, citations, and chunk selection strategies for accurate, low-hallucination answers.
• Experience building and operating evaluation frameworks and search product metrics (relevance, precision/recall, latency, adoption, business impact) to drive continuous iteration.
• Working knowledge of enterprise security and permission-aware retrieval — access controls, tenant isolation, and data governance in multi-tenant SaaS.
• Strong cross-functional collaboration and communication skills, able to translate complex search/AI concepts into scalable roadmaps for both technical and non-technical stakeholders.

We'd especially like to hear from you if:

• You've built or owned enterprise search, knowledge retrieval, or RAG-powered experiences within enterprise collaboration or SaaS applications.
• You have hands-on experience with search platforms and technologies — Elasticsearch/OpenSearch, Solr/Lucene, vector databases (Pinecone, Weaviate, pgvector), or managed enterprise search services.
• You've designed relevance-tuning workflows, feedback loops, or evaluation systems that continuously improve search and RAG quality in production.
• You're excited about AI-native work experiences and making enterprise knowledge instantly findable and trustworthy through assistants like Magnus.