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Job Summary:
ePlus inc. is seeking an elite Solutions Architect to lead the design and deployment of NVIDIA AI Factory-aligned infrastructure. This role involves translating complex AI and machine learning workload requirements into engineered infrastructure solutions and serving as a trusted advisor to enterprise customers.
Responsibilities:
• Lead discovery workshops to capture AI/ML workload requirements, including model training scale, inference SLAs, data pipeline throughput, and multi-tenancy needs.
• Architect full-stack AI Factory solutions aligned to NVIDIA reference architectures, integrating colocation, GPU compute, networking, storage, and software layers.
• Develop detailed Bills of Materials (BOMs), rack elevation diagrams, network topology drawings, and power/cooling budgets for customer proposals.
• Define GPU cluster architectures using NVIDIA DGX, HGX, and MGX systems with B200, B300, and GB300 Blackwell SXM and NVLink-Switch configurations.
• 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.
• Specify colocation requirements including critical power load (MW-scale), UPS and generator configurations, and PUE targets.
• Design high-density GPU deployments utilizing air-cooled, direct liquid cooling (DLC), and rear-door heat exchanger configurations.
• Define meet-me room (MMR) and cross-connect requirements; specify carrier-neutral telecom diversity strategies.
• 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.
• Architect multi-node GPU clusters optimized for large language model (LLM) pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF).
• Size and configure DGX SuperPOD, HGX H/B-series, and MGX modular systems based on model parameter count, dataset size, and iteration timelines.
• Define server firmware, BIOS, BMC, and DGXOS baselines for production GPU infrastructure.
• Establish GPU health monitoring, RAS (Reliability, Availability, Serviceability) policies, and lifecycle management procedures.
• Design backend GPU fabric networks using NVIDIA Quantum InfiniBand (NDR 400Gb/s and HDR 200Gb/s) for distributed training traffic.
• Architect Spectrum-X Ethernet-based AI networking solutions for inference clusters requiring highbandwidth, low-latency connectivity.
• Specify ConnectX-8/7 HCA deployments and configure RDMA over Converged Ethernet (RoCEv2) or InfiniBand transport for NCCL collective operations.
• Integrate BlueField-3 DPUs for GPU-accelerated network functions, storage offload, zero-trust security isolation, and bare-metal provisioning.
• Design leaf-spine and fat-tree topologies for non-blocking bisectional bandwidth in GPU training clusters.
• Define Quality of Service (QoS) policies separating storage, compute fabric, and management plane traffic.
• Design high-performance parallel file system solutions using VAST Data, Hammerspace, and Pure Storage FlashBlade//E for AI training and checkpoint storage.
• Size storage capacity, IOPS, and throughput based on dataset characteristics, checkpoint frequency, and concurrent reader/writer counts.
• Architect multi-tier storage hierarchies: hot NVMe flash (VAST/FlashBlade) for active datasets, warm object storage for model archives, and cold tape/cloud for long-term retention.
• Configure VAST Data Universal Storage for disaggregated storage with NFS, S3, and POSIX access; tune for large sequential read performance.
• Deploy Hammerspace Global Data Environment for distributed data management and NFS-over-RDMA acceleration across geographically dispersed GPU clusters.
• Define data pipeline architectures ingesting from cloud object stores (S3, GCS, ABS) to local flash for GPUlocal data loading without I/O bottlenecks.
• Deploy and configure NVIDIA AI Enterprise (NVAIE) software stack including NVIDIA GPU Operator, NIM microservices, and RAPIDS accelerated data science libraries.
• Architect inference serving infrastructure using NVIDIA NIM (NVIDIA Inference Microservices) for optimized LLM and vision model deployment with autoscaling.
• Implement NVIDIA Dynamo for distributed inference and disaggregated serving of large-scale generative AI models.
• Configure and optimize CUDA toolkit, cuDNN, NCCL communication libraries, and custom kernel environments for training workloads.
• Deploy Base Command Manager and DGXOS for cluster lifecycle management, node provisioning, health dashboards, and job scheduling integration.
• Integrate NVIDIA Mission Control for AI Factory operations, observability, and multi-cluster fleet management.
• Design and deploy Kubernetes-based AI platforms using NVIDIA GPU Operator, integrating with Run:ai for dynamic GPU resource scheduling and multi-tenant workload isolation.
• Configure SLURM workload manager for traditional HPC-style job scheduling on bare-metal GPU clusters, including preemption policies, fair-share scheduling, and burst-to-cloud integration.
• Establish MLOps toolchain integrations with popular frameworks (PyTorch, JAX, TensorFlow) and experiment tracking platforms (MLflow, Weights & Biases).
• Serve as primary technical point of contact throughout the pre-sales and delivery lifecycle, from initial discovery through post-deployment optimization.
• Produce and present architecture design documents, technical proposals, and executive-level briefings to CTO/CIO and VP-level stakeholders.
• Lead proof-of-concept (POC) and pilot deployments, including benchmark design, execution, and results analysis.
• Collaborate with procurement, logistics, and deployment teams to ensure on-time delivery of complex infrastructure programs.
• Provide post-deployment hypercare support, performance tuning, and capacity planning advisory services.
• Contribute to internal knowledge bases, solution playbooks, and reference architectures for repeatable AI Factory deployments.
Qualifications:
Required:
• Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical discipline; Master's degree preferred.
• 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.
• Deep understanding of distributed deep learning concepts: tensor parallelism, pipeline parallelism, data parallelism, gradient checkpointing, and mixed-precision training.
• 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.
• Demonstrated ability to create compelling technical proposals, architecture diagrams (Visio/Lucidchart/draw.io), and BOM-level documentation.
• Exceptional communication skills with proven ability to present to both deep technical audiences and C-level executives.
Preferred:
• NVIDIA-certified professional credentials (DCA-Core, NCP-DS, or equivalent).
• Experience with NVIDIA Base Command Platform or Mission Control for multi-cluster AI Factory operations.
• Familiarity with sovereign AI, government cloud, or regulated industry AI infrastructure requirements.
• Experience integrating AI Factory infrastructure with public cloud (AWS, Azure, GCP) for hybrid and burst-to-cloud architectures.
• Background in MLOps, LLMOps, or platform engineering for production AI model lifecycle management.
• Prior experience with colocation data center procurement, RFP development, and SLA negotiation.
• Contributions to open-source AI infrastructure projects or published technical content (blogs, whitepapers, conference presentations).
• Active participation in the NVIDIA Partner Network (NPN) ecosystem or prior experience at an NVIDIA Elite Solution Provider.
Company:
ePlus is a customer-first, services-led, results-driven and trusted industry leader that helps organizations secure, modernize, optimize, and scale every aspect of their IT infrastructure. Founded in 1990, the company is headquartered in Herndon, USA, with a team of 1001-5000 employees. The company is currently Late Stage.