Staff Backend Engineer - Second Horizon
G
Grafana LabsAI data intelligence
This is a remote opportunity and we are looking for candidates from the U.S. or Canada. Residents of Quebec are not eligible for this role.Full-TimeStaff
SalaryIn Canada, the Base compensation range for this role is CAD $186,368 - $223,642.
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Job Details
- Required Skills
- AWSGCPAzureData engineeringPrompt EngineeringGenerative AI
Requirements
- Experience with LLMs, prompt engineering, and building applications powered by GenAI.
- A proven track record of delivering software that reached production and is actively used by users.
- Exposure to cloud-native environments such as AWS, GCP, or Azure.
- Experience using observability tools to understand and troubleshoot system behavior.
- Strong experience building production-grade, user-facing software systems.
- Familiarity with AI technologies and frameworks, with a practical focus on delivering working solutions.
- Comfort releasing prototypes, collecting feedback, and iterating.
- Ability to take initiative, own projects, work through ambiguity, and define scope when requirements are loosely defined.
- Experience building or working with agent frameworks or multi-agent workflows is a plus.
- Experience with data platforms such as Looker, Tableau, Power BI, Snowflake, or Databricks, or building tools for data engineering, is a plus.
Responsibilities
- Design, implement, test, and operate services for context ingestion, indexing, retrieval orchestration, API access, source configuration, and system administration.
- Build a multi-tenant SaaS architecture that includes tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries.
- Develop APIs and service interfaces for AI agents, MCP tools, CLIs, and internal applications to retrieve context, provenance, confidence signals, and warnings.
- Partner with the team to balance experimentation with long-term reliability as the project moves from prototype to production.
- Instrument services with metrics, logs, traces, alerts, and dashboards, and use observability tools to understand system behavior and improve reliability.
- Shape architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for the new product area.
- Take ownership of AI solutions and ensure they are scalable, maintainable, and aligned with user workflows.
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