Staff AI Engineer | Canada | Remote
About the role
The Opportunity
Grafana Labs is seeking a Staff Engineer (AI & Automation) to own the AI agent infrastructure and automation platform that powers our Marketing Operations organization. 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 Marketing, RevOps, and SDR teams, 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, and Field Operations to build scalable, self-service automation that eliminates manual work and drives operational efficiency.
What You'll Be Doing
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 demand