Staff AI Engineer - 2nd Horizon | UK | Remote

Grafana Labs Grafana Labs · Data AI · EMEA · R&D: Second Horizon

Staff AI Engineer to build and deliver AI solutions, focusing on AI-driven features and AI agents for enterprise data analytics. The role involves rapid experimentation, shipping LLM- or agent-powered workflows, and integrating agentic components with internal tools. Requires strong software engineering skills, experience with LLMs and GenAI applications, and a practical, production-focused mindset.

What you'd actually do

  1. Build and deliver AI solutions: Take ownership of developing delightful, high-performance AI features to help users discover, organize, and optimize access to large datasets.
  2. Rapid experimentation and iteration: Implement a highly iterative process where you quickly prototype, test, and validate with real users, including shipping and evolving LLM- or agent-powered workflows for the data engineering lifecycle.
  3. Collaborate: Work with the rest of the team to shape AI-driven product features, including the integration of agentic components with internal tools like Slack and alerting systems while engaging with internal data teams for dogfooding.
  4. Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows.
  5. Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows.

Skills

Required

  • Experience with LLMs, context engineering, and building applications powered by GenAI.
  • Proven track record of delivering software that made it into production and is actively used by users.
  • Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure).
  • Experience using observability tools to understand and troubleshoot system behavior.
  • Strong engineering skills
  • AI experience with a practical mindset
  • Quick iteration and experimentation
  • Proven initiative
  • Collaborative attitude

What the JD emphasized

  • shipping and scaling impactful features
  • shipping and evolving LLM- or agent-powered workflows
  • delivering high-quality solutions that work in the real world
  • Proven track record of delivering software that made it into production and is actively used by users.

Other signals

  • AI-driven features
  • AI agents
  • LLM-powered workflows
  • GenAI applications