Staff AI Engineer | US | Remote

Grafana Labs

5d•Remote

About The Position

Grafana's Revenue Operations organization is looking for a Staff AI Engineer to own the AI agent infrastructure and automation platform that powers our GTM teams. You'll build multi-agent architectures, LLM integrations, and backend services that connect AI models to internal and third-party data platforms. You'll ship production systems that teams depend on daily. This is a high-autonomy role where you own the technical direction. You'll identify the highest-leverage problems across Sales, Customer Success, and Marketing, design the solutions, and ship them. You'll define the technical direction for the automation platform—data models, API contracts, shared libraries, reference architectures—and partner with Data Engineering, GTM Systems, Field Operations, and GTM leadership to build scalable, self-service automation that eliminates manual work and drives operational efficiency.

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—you can explain complex systems in simple terms to both engineers and business stakeholders

Nice To Haves

Experience with frontend frameworks & tooling (React, Slack Block Kit, dashboard components) to build user-facing interfaces for AI tools
Familiarity with GTM platforms like Salesforce, HubSpot, Outreach, Gainsight, or similar CRM/sales engagement tools
Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, pgvector, or similar)
Prior work automating sales, customer success, or marketing workflows in a B2B SaaS environment
Experience with workflow automation platforms like n8n, Prefect, Clay, PhantomBuster, Apify, Dust, or similar tools
Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources and tools
Exposure to observability tools for AI systems (LangSmith, Weights & Biases, custom logging/evaluation frameworks)
Experience working in Revenue Operations, GTM Analytics, or Sales Operations environments
Previous experience in open source or developer-focused SaaS companies—Grafana is built on OSS and we value engineers who share that DNA

Responsibilities

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
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
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