GL

Staff AI Engineer | US | Remote

United States (Remote) · Remote · full time · Lead / Staff / Principal

$154,445 – $185,334

Listed on Grafana Labs’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

This role builds AI agent infrastructure and automation systems for Grafana's marketing operations, owning multi-agent architectures and backend services that connect LLMs to business platforms. It suits senior engineers with production LLM experience who thrive designing end-to-end systems with minimal direction and want to solve high-leverage automation problems across revenue-generating teams.

Our summary, not Grafana Labs’s wording. The full posting is on their site.

Skills this role names

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What they ask for

Required

8+ years of software engineering experience with depth in backend development, systems integration, or data/analytics engineering
2+ years hands-on experience applying LLMs/AI to production workflows
Strong proficiency in Python and JavaScript/Node.js with Git-based workflows and testing discipline
Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling, structured output parsing, and evaluation
Experience building and operating multi-agent systems at scale including orchestration patterns and state management
Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services
Understanding of LLM failure modes and production mitigations
Proven ability to identify high-leverage problems and deliver end-to-end with minimal direction
Fluent with AI-assisted development tools
Clear technical communicator

Nice to have

Experience with vector databases or retrieval pipelines
Familiarity with marketing or sales platforms like Salesforce, HubSpot, or Marketo
Experience with frontend frameworks like React or Slack Block Kit
Observability tooling for AI systems like LangSmith or Weights & Biases
Experience with workflow orchestration platforms like Temporal, Prefect, or Airflow
Familiarity with Model Context Protocol
Prior work automating marketing, sales, or customer success workflows in B2B SaaS
Active in open-source communities