1. Compute & ClusterManagement
- Architect, configure, and maintain high-density multi-GPU compute clusters (e.g., NVIDIA HGX/DGX architectures).
- Implement and manage container orchestration platforms (Kubernetes, Slurm, or Ray) optimized for AI/ML distributed workloads.
- Monitor GPU health, telemetry, utilization, and thermals minimize idle compute time and prevent single-node bottlenecks.
2. High-Performance Networking& Storage
- Design and optimize low-latency, lossless network fabrics supporting distributed training (InfiniBand, RoCE v2, NVLink, spine-leaf topologies).
- Configure and scale high-throughput parallel file systems and object storage (e.g., Lustre, GPFS/IBM Spectrum Scale, Ceph, MinIO, NVMe-oF) to feed high-speed data pipelines.
3. Automation &Infrastructure as Code (IaC)
- Build and manage automated deployment pipelines using Terraform, Ansible, Helm, or Pulumi.
- Maintain standard golden images, Linux OS tuning (kernel parameters, NUMA node binding, GPU drivers, CUDA/cuDNN libraries), and firmware updates.
4. Operations, Observability& Performance
- Set up end-to-end monitoring, alerting, and metrics dashboards (Prometheus, Grafana, DCGM exporter, NVIDIA System Management Interface).
- Partner with AI/ML engineering teams to diagnose network bottlenecks, NCCL communication latency, and I/O wait states during distributed training jobs.
- Lead incident response, root-cause analysis (RCA), and disaster recovery plans for mission-critical AI environments.
Qualifications &Requirements
Technical Competencies
- Operating Systems: Deep expertise in Linux systems administration, kernel tuning, and shell scripting (Bash/Python).
- Accelerated Compute: Strong understanding of GPU hardware architectures, CUDA runtimes, and PCIe/NVLink topologies.
- Orchestration & Workload Scheduling: Hands-on experience with Kubernetes (GPU operator, device plugins) and/or HPC schedulers (Slurm, Run:ai, Ray).
- High-Speed Networking: Proven experience with RDMA (RoCE v2 / InfiniBand), PFC (Priority Flow Control), and ECN configurations.
- Storage Systems: Familiarity with high-IOPS, low-latency shared storage architectures for AI datasets and model checkpoints.
- Automation: Proficiency in Infrastructure as Code (Terraform) and configuration management (Ansible).
Experience & Education
- Bachelor's Degree in Computer Science, Information Technology, Computer Engineering, or equivalent practical experience.
- 3-6+ years of hands-on experience in infrastructure engineering, high-performance computing (HPC), DevOps, or cloud infrastructure.
- Relevant certifications are a plus (e.g., CKA/CKAD, NVIDIA Certified Associate/Professional, AWS/Azure/GCP Solutions Architect).
