Staff Backend Engineer - Grafana Second Horizon

The Opportunity

At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise.

As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files.

The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership.

We’re looking for a Staff-level Backend Engineer to help build the first production services for this context layer management system. You will design and ship the core backend architecture that powers ingestion, context storage, retrieval APIs, and agent-facing integrations.

What You'll Be Doing

• Build core backend services: Design, implement, test, and operate the first services for context ingestion, context indexing, retrieval orchestration, API access, source configuration, and system administration.
• Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries.
• Power agent-facing retrieval workflows: Build APIs and service interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve relevant context, provenance, confidence signals, and warnings.
• Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability.
• Operate what you build: Instrument services with metrics, logs, traces, alerts, and dashboards using observability tools.
• Contribute to technical direction: Shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices.
• Full ownership: Take full ownership of the AI solutions you develop, ensuring they are scalable, maintainable, and aligned with real user workflows.

What Makes You a Great Fit

• Strong engineering skills: Solid experience building production-grade, user-facing software systems with minimal supervision.
• Practical AI experience: Familiarity with AI technologies and frameworks, focusing on real-world solutions.
• Quick iteration: Comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset.
• Proven initiative: Ability to deal with ambiguity, define scope, and drive impactful projects forward.
• Collaborative attitude: Strong communication skills with an open, solutions-oriented mindset.
• Experience with LLMs, prompt engineering, and GenAI-powered applications.
• Proven track record of delivering production software used actively by users.
• Exposure to cloud-native environments (e.g., AWS, GCP, Azure).

Bonus Points For

• Experience building or working with agent frameworks or multi-agent workflows.
• Experience as a data analyst or working with data platforms (e.g., Looker, Tableau, PowerBI, Snowflake, Databricks).
• Experience building tools for data engineering.