What will your job look like? Build and maintain infrastructure for large‑scale AI and HPC workloads across on‑prem and cloud environments Operate and enhance our multi‑cloud, multi‑cluster scheduling platform Troubleshoot complex issues across the stack: from kernel-level tuning and drivers to networking, storage, and distributed system bottlenecks. Ensure the reliability of critical platform services: queuing systems, time-series databases, and logging pipelines Develop deeply integrated automation and tooling Collaborate with ML engineers and IT engineers to optimize hardware utilization for data intensive workloads Drive best practices in system design, observability, and infrastructure-as-code All you need is: 10+ years of hands‑on experience in SRE, Linux Administration, or Systems Engineering Expert-level Linux knowledge: Deep understanding of system internals, debugging, performance tuning, and the ability to solve failures where hardware meets software. Kubernetes Expertise: Proven experience managing K8s at scale (both managed EKS and bare-metal deployments) Distributed Systems Mastery: Hands-on experience debugging and maintaining:Queuing Systems: RabbitMQ or similar Metrics/Observability Stacks: Prometheus, Thanos, and Grafana, or similar Logging: Elasticsearch or similar Relational Databases: PostgreSQL, or similar Infrastructure-as-Code: Proficiency with Terraform, Helm, and configuration management Networking & Scripting: Strong fundamentals in networking and proficiency in Bash Advantages: Familiarity with GPU/Accelerator scheduling, AI/ML pipelines Experience with multi cloud architectures and hybrid environments Experience with workflow orchestration tools (e.g., Argo Workflows)
