HPC Engineer - AI Workloads & Infrastructure

Job Title: HPC Engineer – AI Workloads & Infrastructure

Department: Operations – High Performance Computing (HPC)

About iVedha:

iVedha

Role Overview:

We are seeking an HPC Engineer to join our operational team supporting AI workloads in a high-performance computing environment. This role focuses on building and managing HPC compute nodes, deploying Kubernetes clusters, and orchestrating bare-metal and virtualized environments. You will also work with advanced storage technologies such as VAST Data and MooseFS, ensuring seamless integration with GPU-accelerated infrastructure.

Key Responsibilities:

• Design, deploy, and maintain HPC clusters for AI/ML workloads, including GPU-accelerated compute nodes (NVIDIA DGX/HGX platforms).
• Implement and manage Kubernetes for containerized AI workloads, ensuring scalability and high availability.
• Configure and optimize bare-metal servers, VMs, and virtualized environments for HPC applications.
• Integrate and manage high-performance storage systems (VAST, MooseFS, Lustre, or similar parallel file systems).
• Implement job scheduling and orchestration using Slurm or equivalent tools for AI and HPC workloads.
• Monitor and tune system performance for GPU utilization, network throughput, and storage I/O.
• Automate deployment and configuration using Forman, Ansible, Terraform, or similar tools.
• Collaborate with AI engineers, DevOps, and data teams to optimize infrastructure for LLM training, fine-tuning, and inference pipelines.
• Ensure security, compliance, and data integrity across HPC environments.

Required Skills & Experience:

• 3+ years in HPC engineering, systems administration, or AI infrastructure roles.
• Strong experience with Linux (RHEL/CentOS/Ubuntu) in HPC environments.
• Hands-on experience with Kubernetes, Docker, and container orchestration for AI workloads.
• Familiarity with GPU clusters, CUDA, NCCL and NVIDIA ecosystem tools.
• Knowledge of high-speed interconnects (InfiniBand, RoCE) and networking for HPC.
• Experience with parallel/distributed file systems (VAST, MooseFS, Lustre, GPFS).
• Proficiency in automation and scripting (Python, Bash, Ansible).
• Understanding of job schedulers (Slurm, PBS, Torque) and workload optimization.

Nice-to-Have:

• Experience with cloud HPC platforms (Azure HPC, AWS ParallelCluster, or similar).
• Familiarity with AI/ML frameworks (PyTorch, TensorFlow) and MLOps pipelines.
• Exposure to observability tools (Prometheus, Grafana) for HPC environments.

Why Join iVedha?

• Work on cutting-edge AI infrastructure projects powering Canada’s sovereign AI ecosystem.
• Collaborate with a world-class team of engineers and AI specialists.
• Competitive compensation, benefits, and opportunities for career growth in HPC and AI.