Principal Solutions Architect

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ePlus is seeking a Principal Solutions Architect to design and deploy NVIDIA AI Factory infrastructure for enterprise and hyperscale customers. This role involves architecting full-stack solutions including GPU compute, high-performance networking, parallel storage, and the NVIDIA AI software stack. You will serve as a trusted technical advisor, leading proof-of-concept deployments and delivering transformative AI infrastructure programs.

• Lead discovery workshops to capture AI/ML workload requirements and architect full-stack AI Factory solutions aligned to NVIDIA reference architectures.
• Develop detailed Bills of Materials, rack elevation diagrams, network topology drawings, and power/cooling budgets.
• Design GPU cluster architectures using NVIDIA DGX, HGX, and MGX systems with Blackwell configurations.
• Specify colocation requirements including critical power load, cooling, and carrier-neutral telecom diversity.
• Design high-performance networking with InfiniBand and Spectrum-X, including RDMA and NCCL optimization.
• Architect parallel storage solutions using VAST Data, Hammerspace, and Pure Storage for AI training workloads.
• Deploy NVIDIA AI Enterprise stack, NIM microservices, and orchestration with Kubernetes and SLURM.
• Serve as primary technical point of contact throughout pre-sales and delivery lifecycle.

• Bachelor's degree in Computer Science, Electrical Engineering, or related technical discipline.
• 8+ years of solutions architecture or systems engineering experience, with at least 4 years focused on GPU infrastructure or HPC.
• Proven track record designing and deploying NVIDIA DGX or HGX-based GPU clusters in production AI/ML environments.
• Deep understanding of distributed deep learning concepts (tensor parallelism, pipeline parallelism, etc.).
• Hands-on experience with InfiniBand or high-speed Ethernet fabric design and RDMA configuration.
• Experience sizing and deploying parallel storage systems for AI workloads.
• Strong working knowledge of Kubernetes, GPU Operator, and at least one GPU workload scheduler (Run:ai or SLURM).
• Excellent communication skills with ability to present to technical and executive audiences.

• NVIDIA-certified professional credentials (DCA-Core, NCP-DS, or equivalent).
• Experience with NVIDIA Base Command Platform or Mission Control.
• Familiarity with sovereign AI, government cloud, or regulated industry AI infrastructure.
• Experience integrating AI Factory infrastructure with public cloud for hybrid architectures.
• Background in MLOps, LLMOps, or platform engineering for production AI model lifecycle management.
• Prior experience with colocation data center procurement and SLA negotiation.
• Contributions to open-source AI infrastructure projects or published technical content.