Used Tools & Technologies
LLM GenAIRequired Skills & Competences
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.
System Administration @ 4
Grafana @ 4
Looker @ 4
Tableau @ 4
GCP @ 4
AWS @ 4
Azure @ 4
Data Engineering @ 4
API @ 4
Experimentation @ 4
Databricks @ 4
Snowflake @ 4
Audit @ 4
Observability @ 4
Generative AI @ 4
AI @ 4
Prompt Engineering @ 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 the company behind the open observability cloud. Grafana Cloud is a fully managed observability platform built for scale with a focus on open source, open standards, and open ecosystems. The company is 100% remote with a global team and serves millions of users and thousands of customers.
This role is part of a skunkworks initiative to bring observability and reliable, governed enterprise context to AI agents and other consumers across the business. The team is building an AI-native data intelligence / context system that provides agents with reliable access to context: data, metadata, definitions, lineage, quality signals, and institutional knowledge. This is an early-stage, high-autonomy role responsible for designing and shipping the first production backend services for that context layer.
Responsibilities
- Design, implement, test, and operate core backend services for context ingestion, indexing, retrieval orchestration, API access, source configuration, and system administration.
- Define and build a scalable SaaS foundation for a multi-tenant service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries.
- Build APIs and service interfaces to power agent-facing retrieval workflows, delivering context, provenance, confidence signals, and warnings to AI agents, MCP tools, CLIs, and internal applications.
- Partner across product and infrastructure to balance fast experimentation with long-term reliability as the project moves from prototype to production.
- Instrument and operate services with metrics, logs, traces, alerts, and dashboards; use observability tools to understand system behavior and improve reliability.
- Contribute to technical direction: architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area.
- Communicate effectively across a dynamic, collaborative environment and take ownership of the solutions you develop.
Requirements
- Strong engineering experience building production-grade, user-facing software systems and delivering software used by real users.
- Experience with LLMs, prompt engineering, and building applications powered by generative AI.
- Exposure to cloud-native environments (AWS, GCP, or Azure).
- Experience using observability tools to understand and troubleshoot system behavior (metrics, logs, traces, alerts, dashboards).
- Comfortable iterating quickly, releasing prototypes, collecting feedback, and maturing systems to be production-grade.
- Demonstrated initiative, ownership, and ability to work through ambiguity.
Nice to have
- Experience building or working with agent frameworks or multi-agent workflows.
- Experience as a data analyst or working with data platforms (Looker, Tableau, PowerBI, Snowflake, Databricks).
- Experience building tools for data engineering.
Compensation & Benefits
- In the US, base compensation range: $174,986 - $209,983. Actual compensation may vary based on level, experience, and skillset. Benefits include equity, bonus (if applicable), and other benefits listed by Grafana Labs.
- 100% remote company; in-person onboarding is provided.
- Global annual leave policy of 30 days per annum (subject to local legislation) with Grafana Shutdown Days.
Additional details
- This is a remote opportunity targeting candidates located in the United States or Canada (residents of Quebec are not eligible).
- Grafana Labs is an equal opportunity employer and may utilize AI tools in its recruitment process.