ePlus Technology

ePlus Technology

Principal Solutions Architect

Irvine, CA · Principal

Sponsorship not specifiedDetected 5 days ago
Node.jsFull-Stack DevelopmentKubernetesHelmLinuxMachine LearningDeep LearningTensorFlowPyTorchData ScienceNLPLLMsMLOpsSalesLogisticsProcurementSystems EngineeringElectrical EngineeringZero TrustCommunication

About the role

  • We are seeking an elite Solutions Architect to lead the end-to-end design, sizing, and deployment of NVIDIA AI Factory-aligned infrastructure. In this highly technical, customer-facing role you will translate complex AI and machine learning workload requirements into fully engineered infrastructure solutions spanning colocation facilities, GPU compute, high-performance networking, parallel storage, and the complete NVIDIA AI software stack.
  • You will serve as a trusted technical advisor to enterprise and hyperscale customers, partnering with sales, product, and engineering teams to win and deliver transformational AI infrastructure programs. Your expertise will directly shape how organizations build and operate production AI Factories capable of training frontier models, running large-scale inference fleets, and accelerating data science pipelines at scale.
  • Your Impact

Responsibilities

  • Solution Design & Architecture
  • Lead discovery workshops to capture AI/ML workload requirements, including model training scale, inference SLAs, data pipeline throughput, and multi-tenancy needs.
  • Develop detailed Bills of Materials (BOMs), rack elevation diagrams, network topology drawings, and power/cooling budgets for customer proposals.
  • Design RTX PRO 6000 Blackwell Server Edition deployments for inference-optimized and enterprise AI workloads.
  • Conduct workload sizing and TCO/ROI modeling to validate infrastructure dimensioning for training, finetuning, and inference at scale.
  • Design high-density GPU deployments utilizing air-cooled, direct liquid cooling (DLC), and rear-door heat exchanger configurations.
  • Engage colocation providers and data center operators to validate capacity availability and negotiate technical SLAs.
  • Coordinate with facilities and MEP engineers to validate power infrastructure from utility feed through PDU to rack level.
  • Design backend GPU fabric networks using NVIDIA Quantum InfiniBand (NDR 400Gb/s and HDR 200Gb/s) for distributed training traffic.
  • Design leaf-spine and fat-tree topologies for non-blocking bisectional bandwidth in GPU training clusters.

Requirements

  • 8+ years of solutions architecture, systems engineering, or technical pre-sales experience, with at least 4 years focused on GPU infrastructure or HPC environments.
  • Proven track record designing and deploying NVIDIA DGX or HGX-based GPU clusters in production AI/ML environments.
  • Hands-on experience with InfiniBand or high-speed Ethernet fabric design, RDMA configuration, and collective communication tuning (NCCL, MPI).
  • Direct experience sizing and deploying parallel storage systems (VAST, Hammerspace, or Lustre/WEKA/GPFS) for AI training workloads.
  • Strong working knowledge of Kubernetes, GPU Operator, and at least one GPU workload scheduler (Run:ai or SLURM).
  • Experience with Linux system administration, CUDA development environment configuration, and GPU driver/firmware management.
  • Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical discipline; Master's degree preferred.

Nice to have

  • Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical discipline
  • Master's degree preferred.

Benefits

  • Architect multi-node GPU clusters optimized for large language model (LLM) pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF).
  • Establish GPU health monitoring, RAS (Reliability, Availability, Serviceability) policies, and lifecycle management procedures.
  • Architect inference serving infrastructure using NVIDIA NIM (NVIDIA Inference Microservices) for optimized LLM and vision model deployment with autoscaling.
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