Principal Product Manager Agents and Context - Elasticsearch

Elastic Elastic · Enterprise · United Kingdom · Enterprise Search - Engineering

Principal Product Manager for Elastic's Agent Builder, focusing on context engineering for AI agents. The role involves defining strategy, roadmap, and execution for capabilities that make AI agents faster, cheaper, and more accurate. It requires deep understanding of the AI/ML landscape, including LLMs, RAG, and vector databases, and involves working closely with customers, sales, engineering, and data science teams.

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 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
  • Outstanding spoken and written communication skills
  • Customer Obsession

Nice to have

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

  • AI Agents
  • Context Engineering
  • RAG
  • Vector Databases
  • Product Management