Responsibilities
- Noz, our AI teammate: agent orchestration, tool calling, streaming over SSE, and session state, the hard parts of agent backends
- SigNoz MCP server: the surface through which any LLM agent works with SigNoz, querying logs, metrics, and traces, and creating alerts, dashboards
- LLM/GenAI observability: monitoring AI apps with OpenTelemetry, ours included
Requirements
- 5-8 years building backend systems in production at product companies with strong engineering culture. You'll work across both Go and Python.
- Strong with Go (concurrency, channels, performance) and with async Python (FastAPI, Pydantic, asyncio), and comfortable designing APIs and data models.
- Curious about building with LLMs and agents. You don't need AI experience, strong backend engineers pick this up fast, and we'll ramp you up. What matters is the drive to make a non-deterministic system behave like a dependable one.
- High ownership in a fast-moving, remote-first team: you drive initiatives from problem discovery → design → implementation → rollout, write clear docs, and explain trade-offs.
Nice to Have
- Shipped LLM features in production: agents, tool or function calling, evals, MCP, context and prompt engineering
- Familiarity with OpenTelemetry and/or ClickHouse, Kafka, Kubernetes, etc.
- Loves open source, ideally with prior contributions to OSS projects (any size)
Benefits
- You'll help define agent-native observability, a new category, not maintain a mature dashboard product.
- If you've been wanting to move into AI engineering, this is a real path in: you bring the backend depth, we bring the AI problems.
- Ship in the open, with a high-caliber team who just can't stop shipping.
- Remote-first, async-friendly culture
Work Arrangement
Remote (Worldwide)
