Staff AI Engineer - 2nd Horizon | Ireland | Remote

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

Grafana Labs is seeking an AI Software Engineer for their skunkworks initiative to build AI-powered features for general data analytics, focusing on making Grafana the best place for humans and AI agents to understand and act on enterprise data. The role involves designing, developing, testing, and shipping AI features, including LLM- or agent-powered workflows, and integrating agentic components with internal tools. The ideal candidate has a strong software engineering background, experience with LLMs and GenAI applications, and a practical, iterative approach to shipping production-grade AI solutions.

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
  • Quick iteration and experimentation
  • Proven initiative
  • Collaborative attitude

Nice to have

  • AI experience with a practical mindset

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