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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Solution Architect - NVIDIA - **Company:** Trick Dog Technology - **Location:** Scottsdale, AZ, United States - **Experience:** Expert - **Salary:** $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computing Platforms, Computer Vision, Cloud Computing, Computer Clusters, Encodings, Nvidia CUDA, Data Centers, Data Security, Linux, Ethernet, General-Purpose Computing on Graphics Processing Units, Identity and Access Management, InfiniBand, Machine Learning, NetApp Applications, Network Diagrams, Network Segmentation, OpenShift, Performance Tuning, Remote Direct Memory Access, Red Hat Enterprise Linux, Ansible, PL-SQL, Weka, AI Infrastructure, Graphics Processing Unit (GPU), High Performance Computing, Data Ingestion, Retrieval-Augmented Generation, Large Language Models, Software Security, Generative AI, Agentic-AI, AI Platforms, Pure Storage, Kubernetes, Infrastructure Automation Frameworks, Slurm, Machine Learning Operations, Hardware Infrastructure, Nim (Programming Language), Api Gateway, Terraform, Data Pipelines, Cisco - **Published:** October 26, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=55a9fae096816115 ## About the Role * 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 ## Description 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.