Senior AI Engineer - Grafana Ops, AI/ML | USA | Remote

Grafana Labs

5h•$154,445 - $185,334•Remote

About The Position

At Grafana, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. The Grafana AI teams play a key role in this mission by helping users make sense of complex observability data through AI-driven features. These capabilities reduce toil, lower the barrier of domain expertise, and surface meaningful signals from noisy environments. What makes our team different is how we work: we operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for an AI Software Engineer with a strong software engineering background, a quick iteration mindset, and a passion for experimentation – balanced by a focus on shipping and scaling impactful features that deliver value to users. You’ll work closely with cross-functional teams to develop, test, and ship AI-powered features that contribute to improving infrastructure and observability quality through automation, while also expanding the capabilities of AI agents across the observability stack to assist users with incident response. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative. We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups—always paired with strong code review and quality standards. You’ll also have access to frontier models (e.g., GPT-Codex 5/3, Claude Opus 4.6, Gemini 3 Pro).

Requirements

Strong engineering skills: Solid experience building production software systems (backend and / or full stack). You’re a self-starter, capable of tackling complex engineering problems with minimal supervision.
AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory.
Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset.
Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined.
Collaborative attitude: You communicate effectively with peers, product managers, and designers. You’re open to feedback, and you bring a solutions-oriented mindset to the table.
Experience with LLMs, prompt 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.

Nice To Haves

Experience building or working with agent frameworks or multi‑agent workflows.
Experience with infrastructure / devops related tooling: Kubernetes, Docker, Terraform or similar for deployments.
Familiarity with model fine-tuning techniques.
Experience building observability tooling.

Responsibilities

Build and deliver AI solutions: Take ownership of developing high-performance AI features to help users detect, triage, and resolve incidents using observability data and tools.
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 incident lifecycle management and automated analysis tasks.
Collaborate cross-functionally: Work with data analysts, product managers, and designers to shape AI-driven product features, including integration of agentic components with internal tools, alerting systems, runbooks, and developer workflows.
Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows.
Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams.
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.