The AI Solutions Architect is responsible for designing, positioning, and guiding the implementation of enterprise AI solutions built around NVIDIA AI technologies, DGX SuperPOD reference architectures, and the NVIDIA technology ecosystem.

This role bridges the gap between customer AI use cases and the infrastructure, platforms, software, and services required to deliver them.

The AI Solutions Architect works directly with customers to understand business objectives, AI workloads, data requirements, performance expectations, and operational constraints, translating those requirements into scalable AI solutions incorporating GPU compute, networking, storage, AI software platforms, data center infrastructure, cloud services, security, and operational services.

The role serves as a technical advisor to customers and a technical leader within the organization's AI practice.

Lead the architecture of enterprise AI solutions supporting:

Translate customer AI use cases into technical requirements across compute, GPU capacity, networking, storage, data, security, software, facilities, and operations.

Determine the appropriate deployment model including:

Develop AI solutions aligned with NVIDIA reference architectures and validated designs.

Architect solutions incorporating technologies such as or equivalent to:

Maintain awareness of the industries evolving AI Factory solutions, architectures and product roadmaps.

Conduct customer workshops to understand business and technical requirements for AI initiatives.

Work with business leaders, infrastructure teams, data scientists, application teams, security teams, and AI/ML engineers to identify:

Translate use cases into an actionable AI architecture and technology roadmap.

Design the underlying infrastructure required to support enterprise AI workloads.

Architect solutions across:

GPU Compute

Design NVIDIA GPU clusters ranging from smaller departmental environments through large-scale AI Factory and DGX SuperPOD deployments.

Networking

Design high-performance AI network fabrics incorporating the following or their industry equivalents:

Storage

Design AI storage platforms optimized for:

Evaluate NVIDIA-certified storage solutions from vendors such as Dell Technologies, NetApp, Pure Storage, VAST Data, WEKA, DDN, IBM, HPE, and other approved ecosystem partners.

Design the platform layer required to operate AI workloads at enterprise scale.

Solutions may incorporate:

Work with AI development teams to ensure infrastructure and platforms are optimized for the intended workloads.

Assist customers in designing the data architecture supporting enterprise AI.

Provide architectural guidance around:

Partner with application, data, and AI specialists where deeper application-level expertise is required.

Incorporate security into AI solution architectures.

Address requirements including:

Work with cybersecurity architects to integrate AI environments into the customer's broader enterprise security architecture.

Assess whether customer facilities can support high-density AI infrastructure.

Provide architectural guidance for:

Identify when customer requirements are better served through colocation, hosted infrastructure, cloud, or GPU-as-a-Service alternatives.

Maintain strong technical relationships with key AI infrastructure partners.

Work with NVIDIA-certified and approved ecosystem vendors across:

Evaluate new technologies and determine their appropriate role within the organization's AI solutions portfolio.

Participate in vendor roadmap sessions, NVIDIA technical briefings, solution validation, partner enablement, and Technical Review Boards.

Serve as the technical lead for strategic AI opportunities.

Responsibilities include:

Communicate complex AI concepts effectively to audiences ranging from engineers and data scientists to CIOs and business executives.

Produce professional customer-facing deliverables including:

Ensure solutions can transition successfully from presales architecture into implementation.

Experience across several of the following technology areas 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 and GPUDirect.

Compute: Dell Technologies, HPE, Lenovo, Supermicro and NVIDIA-certified compute platforms.

Networking: NVIDIA Networking, Arista, Cisco and high-performance Ethernet/InfiniBand.

Storage: NetApp, Pure Storage, Dell, VAST Data, WEKA, DDN, IBM and NVIDIA-certified AI storage platforms.

Platforms: Kubernetes, Red Hat OpenShift, Slurm, Linux and container platforms.

AI/ RAG architectures, vector databases, data pipelines, MLOps, model repositories and enterprise AI platforms.

Automation: Ansible, Terraform and infrastructure orchestration technologies.

Relevant certifications may include:

Equivalent hands-on AI architecture experience may substitute for formal certifications.