Staff AI Engineer

Details

Responsibilities

Agentic Systems & AI Infrastructure

• Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation
• Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams
• Develop reusable agentic skills that agents invoke across interfaces (Slack, dashboards, internal apps, CLIs)
• Implement observability and feedback loops including logging, performance metrics, prompt iteration, model evaluation, and cost management
• Establish governance and compliance standards for AI workflows including access controls, audit trails, PII handling, and human-in-the-loop escalation paths

Systems Integration & Backend Services

• Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools)
• Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal knowledge bases, customer data, and real-time business context
• Build serverless or containerized services (GCP Cloud Functions, Cloud Run) that scale with usage and integrate with Grafana's cloud infrastructure

Automation & Workflow Enablement

• Partner with RevOps, Demand Generation, Regional Marketing, and SDR teams to scope high-impact automation problems, identify bottlenecks, and build solutions with measurable business outcomes
• Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards
• Build systems designed for self-service with documentation, playbooks, and enablement materials that let partner teams operate independently

We invest heavily in developer productivity. You'll have access to AI coding assistants (Claude Code, Gemini CLI, OpenAI Codex, and others of your choice within security guidelines). We encourage pragmatic AI-assisted development paired with strong code review and quality standards.

Requirements

• 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, not just prototypes
• Strong proficiency in Python and JavaScript/Node.js with Git-based workflows, code review practices, and testing discipline
• Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling/tool use, structured output parsing, and evaluation
• Experience building and operating multi-agent systems at scale including agent decomposition, orchestration patterns (sequential chains, router/dispatcher, parallel fan-out), state management, and production monitoring
• You diagnose business problems before writing code. You think in workflows and outcomes, not just functions.
• Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services (Cloud Functions, Cloud Run)
• Understanding of LLM failure modes and production mitigations including confidence thresholds, fallback logic, human escalation, and cost/latency management
• Proven ability to identify high-leverage problems, push back on low-impact requests, and deliver end-to-end with minimal direction
• Fluent with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code). You use AI to build AI systems
• Clear technical communicator who can explain complex systems in simple terms to both engineers and business stakeholders

Benefits

• All roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success
• 100% Remote, Global Culture
• In-Person onboarding
• Balance is Key: global annual leave policy of 30 days per annum, with 3 days reserved for Grafana Shutdown Days