AI Solution Architect - NVIDIA

AI Solution Architecture & Infrastructure Design

• Architect enterprise AI solutions supporting Generative AI, LLMs, RAG, model training, fine-tuning, inference, Agentic AI, machine learning, computer vision, data science, and HPC.
• Design GPU infrastructure ranging from smaller enterprise deployments to large-scale AI Factories and NVIDIA DGX SuperPOD environments.
• Develop solutions utilizing NVIDIA DGX, HGX, GB200/GB300 NVL, H100/H200, Blackwell GPUs, NVLink/NVLink Switch, and NVIDIA reference architectures.
• Design high-performance network fabrics incorporating NVIDIA Spectrum-X, Quantum InfiniBand, BlueField DPUs, ConnectX, GPUDirect, RDMA/RoCE, and leaf/spine architectures.
• Architect AI-optimized storage supporting training datasets, model repositories, checkpointing, RAG, parallel data access, AI pipelines, and long-term retention.
• Evaluate deployment options including on-premises infrastructure, NVIDIA-certified OEM platforms, private/public cloud, colocation, GPU-as-a-Service, and hybrid environments.

AI Platforms, Data & Security

• Design AI platforms incorporating NVIDIA AI Enterprise, NIM, CUDA, NCCL, Base Command, GPU Operator, Network Operator, Kubernetes, Red Hat OpenShift, Slurm, Linux, and containerized GPU environments.
• Provide architectural guidance for MLOps, model repositories, API gateways, vector databases, embeddings, data ingestion, RAG pipelines, AI observability, and infrastructure automation.
• Address enterprise AI security requirements including identity management, data protection, network segmentation, API security, workload isolation, privacy, monitoring, and model/AI governance.
• Evaluate AI Factory and data center requirements including rack density, power, liquid cooling, cabling, fiber, resiliency, and physical security.

Customer Engagement & Presales

• Lead customer discovery sessions and technical workshops to assess AI use cases, model sizes, GPU capacity, training/inference requirements, datasets, performance, security, availability, and scalability.
• Translate business and technical requirements into solution architectures, infrastructure sizing, capacity models, and implementation roadmaps.
• Serve as the technical lead for strategic AI opportunities, including presentations, whiteboarding, demonstrations, proof-of-concept planning, RFP/RFI responses, technical proposals, and executive briefings.
• Develop customer-facing deliverables including high-level and physical/logical designs, GPU cluster architectures, network diagrams, storage designs, Bills of Materials (BOMs), Statements of Work (SOWs), AI readiness assessments, and architecture decision documents.
• Coordinate with engineering, AI/ML, data, security, and implementation teams to ensure successful solution delivery.

Technology Partnerships

• Collaborate with NVIDIA and enterprise technology partners across compute, networking, storage, AI software, cloud, colocation, and managed services.
• Evaluate NVIDIA-certified solutions from Dell Technologies, HPE, Lenovo, Supermicro, NetApp, Pure Storage, VAST Data, WEKA, DDN, IBM, Arista, Cisco, and other ecosystem partners.
• Stay current with NVIDIA product roadmaps, AI Factory architectures, validated designs, and emerging AI infrastructure technologies.

• 8+ years of experience in enterprise infrastructure, data center, cloud, HPC, or solutions architecture.
• Hands-on architecture experience with AI infrastructure, GPU clusters, HPC, or accelerated computing environments.
• Strong understanding of NVIDIA GPU technologies, architectures, and enterprise AI infrastructure.
• Knowledge of Generative AI, LLMs, RAG, model training, fine-tuning, and inference workloads.
• Strong technical background in enterprise compute, storage, high-performance networking, and Ethernet/InfiniBand.
• Experience with Linux, Kubernetes, containerized platforms, and enterprise infrastructure design.
• Experience developing technical architectures, solution designs, and infrastructure sizing recommendations.
• Strong customer-facing consulting, technical presentation, and communication skills.
• Experience collaborating with enterprise OEMs, technology vendors, and engineering teams.

Preferred Technical Experience

Experience with several of the following technologies is highly desirable:

• NVIDIA: DGX, DGX SuperPOD, HGX, GB200/GB300 NVL, H100/H200, Blackwell, Spectrum-X, Quantum InfiniBand, BlueField, ConnectX, NVLink, CUDA, NCCL, AI Enterprise, NIM, GPUDirect, Base Command, GPU Operator, and Network Operator.
• Compute: Dell, HPE, Lenovo, Supermicro, NVIDIA-certified GPU infrastructure.
• Networking: NVIDIA Networking, Cisco, Arista, InfiniBand, RDMA/RoCE, high-performance Ethernet.
• Storage: NetApp, Pure Storage, Dell, VAST Data, WEKA, DDN, IBM, and NVIDIA-certified AI storage.
• Platforms: Kubernetes, Red Hat OpenShift, Slurm, Linux, container orchestration.
• AI/Data: RAG, vector databases, embedding pipelines, MLOps, model repositories, data pipelines, enterprise AI platforms.
• Automation: Ansible, Terraform, infrastructure orchestration.

Preferred Certifications

• NVIDIA AI, AI Infrastructure, or Networking certifications
• Kubernetes or Red Hat certifications
• Cisco CCNP/CCIE
• Dell, Arista, NetApp, Pure Storage, DDN, or other relevant OEM certifications