Principal Product Manager, AI Agents - Search

Elastic Elastic · Enterprise · United States · Enterprise Search - Product Management

Principal Product Manager for Elastic Agent Builder, focusing on defining strategy and execution for context engineering capabilities for AI agents. This role involves understanding customer requirements, market trends, and working with data science and engineering teams to build and evangelize agent context solutions.

What you'd actually do

  1. Work directly with enterprise customers, sales teams, and solution architects to understand requirements, negotiate priorities, clarify product needs
  2. Build, socialize and align a roadmap for core context engineering capabilities built on top of Elastic powered retrieval and relevance for AI Agents
  3. Deeply understand the AI Agent market, major players, trends and how it may impact our strategy
  4. Work directly data science and engineering to build out the strategy for benchmarking and evaluations of agent capabilities
  5. Work with design to build user experiences that address gaps in how agents show and refine context as they work

Skills

Required

  • 10+ years of experience in product management or solution delivery for technical, cloud infrastructure, or platform products
  • Deep technical understanding of the AI/ML landscape, including LLMs, RAG architectures, vector databases, and context engineering
  • Ability to lead across a matrixed organization, align multiple stakeholders toward a common vision, and drive execution
  • Outstanding spoken and written communication skills
  • Customer Obsession

Nice to have

  • Experience with AI tools to accelerate processes and bring clarity to decisions

What the JD emphasized

  • Extensive Experience: 10+ years of experience in product management or solution delivery for technical, cloud infrastructure, or platform products.
  • Deep technical understanding of the AI/ML landscape, including LLMs, RAG architectures, vector databases, and context engineering.

Other signals

  • defining how enterprises build, manage, and scale context for AI agents
  • core context engineering capabilities built on top of Elastic powered retrieval and relevance for AI Agents
  • Deeply understand the AI Agent market, major players, trends and how it may impact our strategy
  • strategy for benchmarking and evaluations of agent capabilities
  • build user experiences that address gaps in how agents show and refine context as they work