Staff Backend Engineer - Grafana Second Horizon | Ireland | Remote

Grafana Labs Grafana Labs · Data AI · Germany, Ireland, Spain, Sweden, United Kingdom · Remote · R&D : Databases

Staff Backend Engineer to build the core backend architecture for an AI-native data intelligence system that provides AI agents with reliable, governed access to enterprise context for retrieval workflows. This includes ingestion, indexing, retrieval APIs, and agent-facing integrations, focusing on building a scalable, multi-tenant SaaS foundation.

What you'd actually do

  1. Build the 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.
  2. 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.
  3. 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.
  4. Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production.
  5. Operate what you build: Instrument services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability.

Skills

Required

  • Strong engineering skills
  • building production-grade, user-facing software systems
  • AI experience with a practical mindset
  • Quick iteration and experimentation
  • Proven initiative
  • deal with ambiguity

Nice to have

  • experience building the first production services for a context layer management system
  • experience building a scalable SaaS foundation
  • experience building APIs and service interfaces for AI agents
  • experience working across product and infrastructure
  • experience operating services with metrics, logs, traces, alerts, and dashboards

What the JD emphasized

  • AI agents
  • context system
  • retrieval APIs
  • backend architecture
  • multi-tenant service
  • agent-facing retrieval workflows
  • AI experience with a practical mindset
  • Quick iteration and experimentation
  • Proven initiative
  • deal with ambiguity

Other signals

  • AI agents
  • context system
  • retrieval APIs
  • backend architecture