Backend Engineer, AI SRE

Build the agent fleet that investigates live production incidents, and the guardrails that make it safe to run.

• Build the agent fleet that investigates live production incidents: gathering context, forming hypotheses, testing them against real telemetry, and synthesising a root cause.
• Design multi-agent orchestration: coordinator loops, specialised sub-agents, and the handoffs between them.
• Wire agents to real observability data through MCP tools: metrics, logs, traces, Kubernetes state, profiles.
• Build the evaluation harnesses that tell us whether an investigation was actually correct, not merely plausible.
• Ship guardrails: least-privilege tool surfaces, read-only boundaries, and permission models that hold under autonomous operation.

• Strong backend engineering first. This is distributed systems work that happens to involve LLMs, not prompt-tuning.
• Experience building agentic systems: tool use, function calling, multi-step planning, long-running loops.
• Judgment about autonomy and safety: what an agent should do unattended, and what it must never do.
• Ability to reason about production incidents yourself. You can’t build an SRE agent without SRE instincts.

• SRE, on-call or incident-response background. You’ve been paged and know what a real investigation feels like.
• Observability depth: correlating metrics, logs and traces to isolate a failure.
• Experience with agent SDKs, MCP, or building tools for LLMs to consume.
• Eval tooling and LLM-as-judge methods, and a healthy scepticism about both.
• Prompts-as-code discipline: versioned, reviewed and tested like any other source.

• Small team, short feedback loops, real ownership from week one.
• You’ll talk to customers, engineers debugging real incidents, and that shapes what you build.
• We ship, then iterate; bias toward hands-on building over process.
• Modern tooling is encouraged, including AI-assisted development.