AI Solutions Architect

Insight Investments Corp • Scottsdale, AZ

3h • $0 - $200,000

About The Position

The AI Solutions Architect designs, positions, and guides implementation of enterprise AI solutions built around NVIDIA AI technologies, DGX SuperPOD reference architectures, and the NVIDIA technology ecosystem. The role connects customer AI use cases with the infrastructure, platforms, software, and services needed to deliver them. It works directly with customers to understand business objectives, AI workloads, data requirements, performance expectations, and operational constraints, then translates those requirements into scalable solutions spanning GPU compute, networking, storage, AI software platforms, data center infrastructure, cloud services, security, and operations. The position serves as a technical advisor to customers and a technical leader within the organization's AI practice.

The architect leads solution design for workloads such as generative AI, LLMs, RAG, model training and fine-tuning, inference, agentic AI, data science, machine learning, computer vision, and HPC. The role determines suitable deployment models, develops architectures aligned with NVIDIA reference designs and validated solutions, and provides guidance on infrastructure, AI platforms, data and RAG, security and governance, and data center readiness.

The position also leads customer discovery and strategic presales engagements, including workshops, presentations, architecture development, solution sizing, bills of materials, proof-of-concept planning, proposal and RFP/RFI responses, statements of work, executive briefings, and demonstrations. It produces customer-facing architecture and planning documents and works with technology partners and internal specialists to support successful transition from presales architecture into implementation.

Requirements

• 8+ years of experience in enterprise infrastructure, cloud, data center, HPC, or solution architecture.
• Experience designing AI, GPU, HPC, or accelerated-computing environments.
• Strong knowledge of NVIDIA technologies and GPU architectures.
• Understanding of generative AI, LLM, RAG, training, fine-tuning, and inference workloads.
• Strong knowledge of enterprise compute, networking, and storage.
• Experience with high-performance Ethernet and/or InfiniBand.
• Understanding of Kubernetes and containerized environments.
• Knowledge of Linux infrastructure.
• Experience designing enterprise architectures and technical solutions.
• Strong customer-facing consulting and presentation skills.
• Experience working with enterprise OEMs and technology partners.

Nice To Haves

• Experience across several of the listed NVIDIA technology areas, including DGX, DGX SuperPOD, HGX, GB200/GB300 NVL, H100/H200, Blackwell, Spectrum-X, Quantum InfiniBand, BlueField, ConnectX, NVLink, CUDA, NCCL, AI Enterprise, NIM, and GPUDirect.
• Experience with compute platforms from Dell Technologies, HPE, Lenovo, Supermicro, or NVIDIA-certified providers.
• Experience with NVIDIA Networking, Arista, Cisco, high-performance Ethernet, or InfiniBand.
• Experience with NetApp, Pure Storage, Dell, VAST Data, WEKA, DDN, IBM, or NVIDIA-certified AI storage platforms.
• Experience with Kubernetes, Red Hat OpenShift, Slurm, Linux, or container platforms.
• Experience with RAG architectures, vector databases, data pipelines, MLOps, model repositories, or enterprise AI platforms.
• Experience with Ansible, Terraform, or infrastructure orchestration technologies.
• Relevant NVIDIA AI, AI Infrastructure, or networking certifications; Kubernetes or Red Hat certifications; Cisco CCNP/CCIE; or Dell, DDN, Arista, NetApp, Pure Storage, or other relevant OEM certifications. Equivalent hands-on AI architecture experience may substitute for formal certifications.

Responsibilities

• Lead architecture of enterprise AI solutions for generative AI, LLMs, RAG, model training, fine-tuning, inference, agentic AI, data science, machine learning, computer vision, and HPC.
• Translate customer AI use cases into technical requirements across compute, GPU capacity, networking, storage, data, security, software, facilities, and operations.
• Determine appropriate deployment models, including customer-owned infrastructure, NVIDIA DGX and DGX SuperPOD, NVIDIA-certified OEM infrastructure, colocation, private or public cloud, GPU-as-a-Service, and hybrid AI environments.
• Develop AI solutions aligned with NVIDIA reference architectures and validated designs, and maintain awareness of evolving AI Factory architectures and product roadmaps.
• Conduct customer workshops and collaborate with business, infrastructure, data, application, security, and AI/ML teams to identify objectives, workloads, datasets, performance, security, availability, and growth requirements.
• Translate discovered requirements into actionable AI architectures and technology roadmaps.
• Design GPU compute clusters and AI network fabrics, and design storage platforms for training datasets, model repositories, checkpointing, RAG data, pipelines, and retention.
• Evaluate NVIDIA-certified storage solutions and other approved ecosystem technologies.
• Design enterprise AI platform layers, potentially including NVIDIA AI Enterprise, NIM, Kubernetes, OpenShift, Slurm, MLOps, model repositories, API gateways, vector databases, data pipelines, observability, and infrastructure automation.
• Work with AI development teams to optimize infrastructure and platforms for intended workloads.
• Provide guidance on enterprise data sources, ingestion, pipelines, storage, vector databases, embeddings, RAG architectures, data access, security, and lifecycle management; partner with specialists for deeper application-level expertise.
• Incorporate identity and access management, data and API security, network segmentation, privacy, workload isolation, logging, monitoring, and AI and model governance into architectures.
• Work with cybersecurity architects to align AI environments with enterprise security architecture.
• Assess customer facilities for high-density AI infrastructure and advise on rack density, power, cooling, liquid cooling, power distribution, cabling, fiber, layouts, resiliency, and physical security.
• Identify when colocation, hosted infrastructure, cloud, or GPU-as-a-Service is a better fit than customer facilities.
• Maintain technical relationships with AI infrastructure partners, evaluate technologies for the solutions portfolio, and participate in roadmap sessions, technical briefings, solution validation, partner enablement, and Technical Review Boards.
• Serve as technical lead for strategic AI opportunities, leading discovery, strategy workshops, presentations, whiteboarding, architecture, sizing, bills of materials, proof-of-concept planning, RFP/RFI responses, proposals, statements of work, executive briefings, and demonstrations.
• Communicate complex AI concepts to engineers, data scientists, CIOs, and business executives.
• Produce customer-facing AI architectures, high-level designs, logical and physical diagrams, AI Factory designs, network topologies, storage and GPU cluster architectures, bills of materials, capacity models, readiness assessments, implementation roadmaps, and architecture decision documents.
• Ensure solutions transition successfully from presales architecture into implementation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed