Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
API
AWS @ 4
Audit
Azure @ 4
BI @ 4
Communication @ 7
Data Engineering @ 4
Databricks @ 4
Experimentation
GCP @ 4
GenAI
Generative AI @ 4
Grafana @ 4
LLM
Looker @ 4
Observability @ 4
Power BI @ 4
Prompt Engineering @ 4
Snowflake @ 4
System Administration
Tableau @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Grafana Labs is building an AI-native data intelligence system that gives AI agents reliable, governed access to enterprise context, including data, metadata, definitions, lineage, quality signals, and institutional knowledge. The context system retrieves information, agents decide, and data teams maintain the intelligence.
This role will help build the first production services for the context layer management system. You will design and ship backend architecture for ingestion, context storage, retrieval APIs, and agent-facing integrations. The role involves working through ambiguity, making pragmatic architectural decisions, and evolving systems from internal dogfooding to production-grade SaaS.
Responsibilities
- Design, implement, test, and operate backend services for context ingestion, indexing, retrieval orchestration, API access, source configuration, and system administration.
- Define and build a scalable, multi-tenant SaaS foundation, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries.
- Build APIs and service interfaces for AI agents, MCP tools, CLIs, and internal applications to retrieve context, provenance, confidence signals, and warnings.
- Balance rapid 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.
- Contribute to architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices.
- Communicate effectively and collaborate across teams in a dynamic environment.
- Take ownership of AI solutions, ensuring they are scalable, maintainable, and aligned with real user workflows.
Requirements
- Strong experience building production-grade, user-facing software systems.
- Ability to tackle complex engineering problems, make decisions independently, and work with minimal supervision.
- Familiarity with AI technologies and frameworks, with a practical focus on delivering high-quality real-world solutions.
- Experience quickly prototyping, collecting feedback, and iterating pragmatically.
- Proven initiative, ownership, and ability to define scope in ambiguous environments.
- Strong communication and collaboration skills.
- Experience with LLMs, prompt engineering, and building applications powered by generative AI.
- A proven track record of delivering software that reached production and is actively used by customers or users.
- Experience working in cloud-native environments such as AWS, GCP, or Azure.
- Experience using observability tools to understand and troubleshoot system behavior.
Bonus Points
- Experience building or working with agent frameworks or multi-agent workflows.
- Experience as a data analyst or working with data platforms such as Looker, Tableau, Power BI, Snowflake, or Databricks.
- Experience building tools for data engineering.
Working Environment
Grafana Labs is a remote-first company. The team meets regularly over video and does most work asynchronously and in writing. The role offers a high degree of autonomy and ownership in a collaborative environment.
Compensation and Benefits
- Sweden base compensation range: SEK 878,578–SEK 1,054,294 per year.
- Equity, bonus where applicable, and other benefits.
- 30 days of annual leave per year, with three days reserved for Grafana Shutdown Days, subject to local legislation.
- In-person onboarding.
- Career growth opportunities and a global remote culture.