AI Solutions Architect

Houston, TX, US

5 days ago Requisition

Salary: $ Annually

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:

• Generative AI
• Retrieval-Augmented Generation (RAG)
• AI inference
• Agentic AI
• Machine learning
• High-Performance Computing (HPC)

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:

• Customer-owned AI infrastructure
• NVIDIA DGX-based infrastructure
• NVIDIA DGX SuperPOD
• NVIDIA-certified OEM infrastructure
• Colocation
• Private cloud
• Public cloud
• GPU-as-a-Service

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

Architect solutions incorporating technologies such as or equivalent to:

• NVIDIA DGX
• NVIDIA DGX SuperPOD
• NVIDIA HGX
• NVIDIA GB200/GB300 NVL systems
• NVIDIA H100/H200 and Blackwell-generation GPUs
• NVIDIA NVLink and NVLink Switch
• NVIDIA Spectrum-X
• NVIDIA Blue Field DPUs
• NVIDIA ConnectX
• NVIDIA AI Enterprise
• NVIDIA NIM
• NVIDIA CUDA
• NVIDIA NCCL
• NVIDIA Base Command
• NVIDIA GPU Operator
• NVIDIA Network Operator

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:

• Business objectives
• AI use cases
• Training requirements
• Inference requirements
• RAG requirements
• Performance requirements
• Security and governance requirements
• Availability requirements

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:

• NVIDIA Spectrum-X Ethernet
• RDMA/RoCE
• NVIDIA Blue Field
• NVIDIA ConnectX
• GPUDirect
• High-performance Ethernet

Storage

Design AI storage platforms optimized for:

• Checkpointing
• RAG data
• High-performance parallel access

Evaluate NVIDIA-certified storage solutions from vendors such as Dell Technologies, Net App, 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:

• NVIDIA AI Enterprise
• NVIDIA NIM
• Red Hat Open Shift
• MLOps platforms
• API gateways
• AI observability

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:

• Object and file storage
• Model access to enterprise data
• Data security

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

Incorporate security into AI solution architectures.

Address requirements including:

• Identity and access management
• Data security
• API security
• Data privacy
• Logging and monitoring
• AI governance

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:

• Power
• Cooling
• Network cabling
• Fiber…