Site Reliability Engineer - AI Infrastructure

Are you looking for an exciting new opportunity? Join a seed-stage AI infrastructure company building large-scale training and inference platforms previously accessible only to hyperscalers. The business began with a single managed GPU cluster that reached capacity almost immediately and has since expanded into a global platform spanning infrastructure, networking, and orchestration. Responsibilities Design, deploy, and maintain large-scale GPU clusters (H100/H200/B200) for training and inference workloads. Build automation pipelines for provisioning, scaling, and monitoring compute resources across Slurm and Kubernetes environments. Develop observability, alerting, and auto-healing systems for high-availability GPU workloads. Collaborate with ML, networking, and platform teams to optimize resource scheduling, GPU utilization, and data flow. Implement infrastructure-as-code, CI/CD pipelines, and reliability standards across thousands of nodes. Diagnose performance bottlenecks and drive continuous improvements in reliability, latency, and throughput. Skills / Must Have 7+ years of experience in SRE, DevOps, or Infrastructure Engineering roles supporting large-scale compute environments. Strong hands‑on experience with Kubernetes and Slurm for cluster orchestration and workload management. Deep knowledge of Linux systems, networking, and GPU infrastructure (NVIDIA H100/H200/B200 preferred). Proficiency in Python, Go, or Bash for automation, tooling, and performance tuning. Experience with observability stacks (Prometheus, Grafana, Loki) and incident response frameworks. Familiarity with high-performance computing (HPC) or AI/ML training infrastructure at scale. Background in reliability engineering, distributed systems, or hardware acceleration environments is a strong plus. Benefits IPO Equity 10% company bonus 401K 4% match Salary $250,000 gross per year #J-18808-Ljbffr Hamilton Barnes Associates Limited