Agentic Engineering Platform

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.

What is The Role

AI is fundamentally transforming how professional services teams build and deliver value. At Elastic, we are focused on creating production-grade services platforms and software engineering practices that work reliably in real-world environments. The agentic engineering platform is a suite of tools designed to automate complex, multi-step delivery workflows across our global services organization.

We are looking for a Principal, Agentic Engineering Platform to lead this initiative end-to-end. In this role, you will design, ship, and refine agentic systems that directly support our services team. You will set technical standards, guide product strategy, and manage the complete life cycle: building, testing, deploying, monitoring, and iterating. While automated agents expand what you can deliver, you are the architect and decision-maker responsible for the final outcome. A key early priority is DIMA, Elastic's migration automation platform for SIEM and observability projects. Experience in security, SIEM, or observability will be highly valuable.

What You Will Be Doing

• Services Product Engineering & Platform Leadership:

  • Establish the technical vision and architectural standards for services agentic systems, ensuring automated tools enhance rather than replace engineering judgment.
  • Manage the platform roadmap and release schedule, overseeing stages from discovery and classification to quality assurance and rollout.
  • Develop multi-step workflows using LLM orchestration, tool integration, retrieval-augmented generation (RAG), and structured output processing.
  • Guide AI-assisted development workflows with tools like Claude Code and Codex, emphasizing issue-focused tasks, automated testing, and security reviews.
  • Design robust system architecture that addresses operational boundaries, potential failure modes, human approval steps, and long-term maintainability.

  Evaluation, Quality & Observability: