Senior AI Engineer | Canada | Remote

Grafana Labs Grafana Labs · Data AI · Canada, United States · Remote · Marketing

Senior AI Engineer role focused on building and owning the AI agent infrastructure and automation platform for Marketing Operations. This involves developing multi-agent architectures, LLM integrations, and backend services, shipping production systems, and defining the technical direction for the automation platform. The role requires integrating AI models with internal and third-party data platforms and partnering with various teams to build scalable, self-service automation.

What you'd actually do

  1. Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation
  2. Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams
  3. Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools)
  4. 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
  5. Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards

Skills

Required

  • Python
  • JavaScript/Node.js
  • Git-based workflows
  • code review practices
  • testing discipline
  • LLM frameworks
  • prompt engineering
  • RAG
  • function calling/tool use
  • structured output parsing
  • evaluation
  • multi-agent systems
  • agent decomposition
  • orchestration patterns
  • state management
  • production monitoring
  • Google Cloud Platform
  • BigQuery
  • serverless/containerized services (Cloud Functions, Cloud Run)
  • LLM failure modes
  • production mitigations
  • confidence thresholds
  • fallback logic
  • human escalation
  • cost/latency management
  • AI-assisted development tools

Nice to have

  • LangChain
  • CrewAI
  • Anthropic MCP
  • n8n
  • Workato
  • Grafana's cloud infrastructure

What the JD emphasized

  • own the AI agent infrastructure and automation platform
  • build multi-agent architectures
  • LLM integrations
  • ship production systems
  • own the technical direction
  • define the technical direction for the automation platform
  • build scalable, self-service automation
  • Own end-to-end development of multi-agent AI systems
  • Build modular, composable agentic systems
  • Build MCP servers, APIs, CLIs, and microservices
  • Architect data flows for retrieval-augmented generation (RAG)
  • Build systems designed for self-service
  • 2+ years hands-on experience applying LLMs/AI to production workflows, not just prototypes
  • Experience building and operating multi-agent systems at scale
  • Understanding of LLM failure modes and production mitigations
  • Fluent with AI-assisted development tools

Other signals

  • building multi-agent architectures
  • LLM integrations
  • backend services that connect AI models to internal and third-party data platforms
  • ship production systems
  • own the technical direction
  • define the technical direction for the automation platform
  • partner with Data Engineering, GTM Systems, and Field Operations to build scalable, self-service automation