Principal Solutions Architect (Req#1048) (San Ramon)

Principal Solutions Architect (Req#1048)

San Ramon, CA

Overview

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

• 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.

Colocation & Facility Planning

• 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.

GPU Compute Infrastructure

• 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.

High-Performance Networking

• 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.

AI Software Stack & Orchestration

• 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.

Candidates must demonstrate deep, hands-on expertise across the following technology domains:

GPU Compute

DGX B200 / B300, DGX H100 / H200, HGX B200 / B300, HGX H100 / H200,

MGX platforms, GB300 NVL72 / GB200 NVL72, RTX PRO 6000 Blackwell

Server Edition, NVLink Switch System, NVLink-C2C

Networking

NVIDIA Quantum InfiniBand (NDR 400G, HDR 200G), Spectrum-X Ethernet, ConnectX-8 / ConnectX-7 HCAs, BlueField-3 DPU, SHARP in-network computing, UFM Fabric Manager, RDMA / RoCEv2 / InfiniBand

Storage

VAST Data Universal Storage (NFS/S3/POSIX), Hammerspace Global Data Environment, Pure Storage FlashBlade//E (Evergreen//One), NFS-over-RDMA, parallel file systems (Lustre, GPFS/WEKA), S3-compatible object storage

AI Software

NVIDIA AI Enterprise (NVAIE), NIM Microservices, RAPIDS (cuDF, cuML, cuGraph), NVIDIA Dynamo, CUDA Toolkit, cuDNN, NCCL, TensorRT, Triton Inference Server

Base Command Manager, DGXOS, NVIDIA Mission Control, DGX Cloud, UFM, IPMI / Redfish BMC management

Orchestration

Colocation

Frameworks

PyTorch, JAX, TensorFlow, Hugging Face Transformers, DeepSpeed, Megatron-LM, vLLM, LMDeploy

Qualifications

• 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]]>