Cognizant
Overview
Cognizant is seeking a highly skilled hands-on Infrastructure Engineer with proven experience in the physical and technical deployment of AI-ready environments optimized for AI and machine learning workloads. This role focuses on NVIDIA DGX or similar systems, GPU-accelerated compute clusters, high-speed networking, and scalable storage solutions. The ideal candidate will have deep expertise in infrastructure design, deployment, workload orchestration, and performance optimization in enterprise environments. This is a remote role in the US. Salary range for this role is between $99,000 and $116,000 depending on skills and qualifications of the candidate. Applications will be accepted till 10/21/2025. Key Responsibilities
System Design & Deployment
Help in rightsizing GPU investment Architect and deploy NVIDIA DGX systems and GPU-based compute clusters. Design and implement scalable parallel filesystems (e.g., Lustre, BeeGFS, GPFS). Integrate high-speed interconnects using InfiniBand, RoCE, and RDMA. Collaborate on rack planning and airflow optimization. Cluster & Infrastructure Management
Configure and manage Slurm Workload Manager for job scheduling. Deploy and maintain cluster orchestration tools Automate provisioning using PXE boot, Terraform, Redfish, and Kubernetes. Perform firmware updates, BIOS/IPMI/BMC configuration, and OS provisioning Knowledge of Run.ai, ClearML or similar platform Networking & Performance Optimization
Design and validate network topologies including IPMI, internal/external networks, and InfiniBand fabrics. Optimize RDMA and RoCE configurations for low-latency, high-throughput data transfers. Conduct performance benchmarking using GPU-Burn, NCCL, and NVSM. Monitoring & Troubleshooting
Implement system health checks and diagnostics across compute, storage, and network layers. Troubleshoot hardware/software issues and ensure reliable infrastructure operation. Required Skills & Qualifications
Technical Expertise
Deep understanding of NVIDIA DGX architecture, CUDA, and GPU compute. Strong Linux system administration and shell scripting skills. Experience with Slurm, parallel filesystems, and high-speed networking (InfiniBand/RDMA/RoCE). Familiarity with containerization (Docker), orchestration (Kubernetes), and automation tools (Ansible, Redfish). Preferred Qualifications
Experience with BBCM, and DGX BasePOD/SuperPOD configuration Certifications by Nvidia or equivalent OEM.
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Cognizant is seeking a highly skilled hands-on Infrastructure Engineer with proven experience in the physical and technical deployment of AI-ready environments optimized for AI and machine learning workloads. This role focuses on NVIDIA DGX or similar systems, GPU-accelerated compute clusters, high-speed networking, and scalable storage solutions. The ideal candidate will have deep expertise in infrastructure design, deployment, workload orchestration, and performance optimization in enterprise environments. This is a remote role in the US. Salary range for this role is between $99,000 and $116,000 depending on skills and qualifications of the candidate. Applications will be accepted till 10/21/2025. Key Responsibilities
System Design & Deployment
Help in rightsizing GPU investment Architect and deploy NVIDIA DGX systems and GPU-based compute clusters. Design and implement scalable parallel filesystems (e.g., Lustre, BeeGFS, GPFS). Integrate high-speed interconnects using InfiniBand, RoCE, and RDMA. Collaborate on rack planning and airflow optimization. Cluster & Infrastructure Management
Configure and manage Slurm Workload Manager for job scheduling. Deploy and maintain cluster orchestration tools Automate provisioning using PXE boot, Terraform, Redfish, and Kubernetes. Perform firmware updates, BIOS/IPMI/BMC configuration, and OS provisioning Knowledge of Run.ai, ClearML or similar platform Networking & Performance Optimization
Design and validate network topologies including IPMI, internal/external networks, and InfiniBand fabrics. Optimize RDMA and RoCE configurations for low-latency, high-throughput data transfers. Conduct performance benchmarking using GPU-Burn, NCCL, and NVSM. Monitoring & Troubleshooting
Implement system health checks and diagnostics across compute, storage, and network layers. Troubleshoot hardware/software issues and ensure reliable infrastructure operation. Required Skills & Qualifications
Technical Expertise
Deep understanding of NVIDIA DGX architecture, CUDA, and GPU compute. Strong Linux system administration and shell scripting skills. Experience with Slurm, parallel filesystems, and high-speed networking (InfiniBand/RDMA/RoCE). Familiarity with containerization (Docker), orchestration (Kubernetes), and automation tools (Ansible, Redfish). Preferred Qualifications
Experience with BBCM, and DGX BasePOD/SuperPOD configuration Certifications by Nvidia or equivalent OEM.
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