HPC Specialist

🔬 HPC Specialist – Role Overview

Arcadion is seeking an HPC (High-Performance Computing) Specialist responsible for the design, deployment, optimization, and management of high-performance computing systems, often used in scientific research, engineering simulations, AI/ML workloads, and large-scale data analytics.

🧠 Core Responsibilities

1. Architecture & System Design
   • Design scalable HPC clusters (on-prem, cloud, or hybrid)
   • Choose appropriate CPUs, GPUs, interconnects (e.g., InfiniBand), and storage
   • Configure Slurm, PBS, or OpenHPC job schedulers
2. Cluster Deployment & Maintenance
   • Install and manage Linux-based compute nodes
   • Maintain job schedulers and resource managers
   • Integrate monitoring tools (Prometheus, Grafana, Nagios)
3. Performance Tuning & Optimization
   • Benchmark workloads and tune for performance (e.g., MPI, CUDA, OpenMP)
   • Optimize I/O and inter-node communication
   • Ensure efficient job execution and queue handling
4. Cloud & Hybrid HPC Integration
   • Deploy and manage cloud-based HPC environments (Azure CycleCloud, AWS ParallelCluster, Google Cloud HPC Toolkit)
   • Optimize workload portability and orchestration (e.g., Singularity, Kubernetes with KubeFlow or Volcano)
5. AI/ML & GPU Workload Support
   • Manage AI pipelines that require HPC acceleration (e.g., LLM training)
   • Optimize GPU usage (NVIDIA A100/H100, AMD MI300)
   • Interface with TensorFlow, PyTorch, and HPCML tools
6. Security & Compliance
   • Implement security best practices for multi-user environments
   • Support data governance for sensitive or regulated workloads
   • Maintain audit trails and role-based access control
7. User & Application Support
   • Assist researchers and data scientists with job submissions and optimization
   • Develop documentation, training materials, and run code validation sessions

🛠️ Technical Skills & Tools Category Technologies Schedulers Slurm, PBS, Torque, LSF HPC OS & Config RHEL/CentOS, Rocky, Ubuntu Server HPC File Systems Lustre, BeeGFS, GPFS Parallel Computing MPI, OpenMP, CUDA Monitoring/Telemetry Prometheus, Grafana, Ganglia Cloud HPC AWS HPC, Azure CycleCloud, GCP HPC Toolkit Containers Singularity, Apptainer, Docker, Kubernetes AI/ML Support PyTorch, TensorFlow, Horovod, MLFlow DevOps Tools Ansible, Terraform, Git, Jenkins

🎓 Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, Physics, or a related technical field
• 3–7 years of experience in HPC environments
• Experience supporting AI/ML teams is a major asset
• Certifications: NVIDIA DLI, AWS Certified HPC Specialist, Linux+ or RHCE