Principal Product Manager, AI Agents - Search

Elastic Elastic · Enterprise · San Francisco, CA · Enterprise Search - Product Management

Principal Product Manager for Elastic Agent Builder, focusing on defining how enterprises build, manage, and scale context for AI agents. This role involves understanding customer requirements, building roadmaps for context engineering capabilities, and working with data science and engineering on agent benchmarking and evaluations.

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
  • Consistent record of leading sophisticated, data-intensive products from inception through launch and iterative growth
  • Deep technical understanding of the AI/ML landscape, including LLMs, RAG architectures, vector databases, and context engineering
  • Comfortable working closely with engineers and data scientists to solve intricate technical challenges
  • Able to move fast and quickly learn from experiments and tests
  • Demonstrated ability to lead across a matrixed organization, align multiple stakeholders toward a common vision, and drive execution in a fast-paced, remote-first environment
  • Outstanding spoken and written communication skills
  • Ability to distill complex engineering details into compelling narratives for both technical and non-technical audiences, including executive leadership
  • Customer Obsession

Nice to have

  • Utilize AI tools to help accelerate your processes and bring clarity to your decisions

What the JD emphasized

  • AI Agent Builder
  • context layer
  • build, manage, and scale context for AI agents
  • Deeply understand the AI Agent market
  • benchmarking and evaluations of agent capabilities
  • LLMs, RAG architectures, vector databases, and context engineering

Other signals

  • AI agents
  • context engineering
  • retrieval and relevance for AI Agents
  • benchmarking and evaluations of agent capabilities