Senior Software Engineer — Infra Agent Systems
Build production AI agents and the distributed platform that operates large-scale GPU infrastructure. The role requires 5+ years of backend, distributed systems, or infrastructure experience, with expertise in agent systems, knowledge graphs, retrieval, or semantic search.
About the job
Responsibilities
- Design and build production AI agent systems that diagnose, investigate, and remediate infrastructure issues across a large GPU fleet.
- Build distributed services, orchestration frameworks, knowledge graphs, and retrieval systems that power infrastructure agents.
- Develop fleet intelligence systems combining telemetry, infrastructure state, operational knowledge, and historical incidents.
- Integrate with observability, incident management, ticketing, fleet inventory, source control, chat, and internal infrastructure systems through APIs.
- Own services end to end, including architecture, implementation, testing, deployment, observability, and production operations.
- Improve agent performance through evaluations, retrieval improvements, better tools, and production feedback loops.
- Turn production agent learnings into reliable, reviewed software and automation.
Requirements
- 5+ years of experience building production backend systems, distributed systems, or infrastructure platforms.
- Strong systems design skills and experience owning significant systems from design through production.
- Depth in at least one of: AI agent systems, orchestration, tool use, evaluation, grounding, knowledge graphs, graph data modeling, search, retrieval, ranking, RAG, or semantic search systems.
- Strong backend engineering experience, including API design, service boundaries, data modeling, and complex-system integrations.
- Experience with Kubernetes, GitOps such as ArgoCD, infrastructure-as-code, and cloud platforms.
- Comfort working with Go, TypeScript, Python, or Rust.
Nice-to-haves
- GPU infrastructure, datacenters, bare-metal systems, hardware failure modes, BMC/IPMI, or cluster schedulers.
- Graph databases.
- Event-driven systems and messaging platforms such as NATS or Kafka.
- Observability platforms such as Prometheus and Grafana.
- Building evaluation frameworks or improving the quality and reliability of LLM-powered systems.
Compensation and Benefits
- Compensation and benefits details were not provided.
Skills
AI Agents, Distributed Systems, Backend Engineering, Knowledge Graphs, Information Retrieval, Kubernetes, Argo CD, Infrastructure As Code, Go, TypeScript, Python, Rust, Graph Databases, Kafka, Prometheus
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