Staff AI Engineer - 2nd Horizon | Germany | Remote

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

Staff AI Engineer role focused on building AI-powered features and agentic workflows for general data analytics within an observability platform. The role involves rapid experimentation, shipping production-grade AI solutions, and integrating AI agents with internal tools.

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
  • 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
  • production-grade software systems
  • AI technologies and frameworks
  • Quick iteration and experimentation

Nice to have

  • UI design decisions with minimal supervision
  • AI and automation tools to enhance product functionality and development workflows

What the JD emphasized

  • strong software engineering background
  • shipping and scaling impactful features
  • AI-powered features
  • large datasets
  • analytics-focused AI agents
  • strong engineering skills
  • production-grade, user-facing software systems
  • AI technologies and frameworks
  • delivering high-quality solutions
  • shipping prototypes
  • Proven initiative
  • take ownership
  • drive projects forward
  • Experience with LLMs
  • context engineering
  • building applications powered by GenAI
  • delivering software that made it into production
  • actively used by users

Other signals

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