Staff GTM Engineer (AI & Automation)
New
G
Grafana LabsB2B SaaS Observability
This is a remote opportunity and we are looking for candidates from Canada.Full-TimeStaff
SalaryCAD 186368 - 223642 / year
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Job Details
- Experience
- 8+ years of software engineering experience
- Required Skills
- Node.jsPythonGCPJavascriptCI/CDMicroservicesBigQueryPrompt EngineeringLLM
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.
- 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, and structured output parsing.
- Experience building and operating multi-agent systems at scale including state management and production monitoring.
- Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services like Cloud Functions and Cloud Run.
- Understanding of LLM failure modes and production mitigations including confidence thresholds and cost management.
- Proven ability to identify high-leverage business problems and deliver end-to-end solutions.
- Experience with AI-assisted development tools like GitHub Copilot, Cursor, or Claude Code.
- Clear technical communicator able to explain complex systems to both engineers and business stakeholders.
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 that operate 24/7 across teams.
- Develop reusable agentic skills that agents invoke across interfaces like Slack, dashboards, and internal apps.
- Implement observability and feedback loops including logging, performance metrics, prompt iteration, and cost management.
- Build APIs, CLIs, and microservices connecting AI models to internal business systems like BigQuery, Slack, and CRMs.
- Architect data flows for retrieval-augmented generation (RAG) connecting LLMs to internal knowledge bases and business data.
- Design and deploy workflows using orchestration tools with CI/CD and production reliability standards.
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