Job Description
Job DescriptionPosition Summary
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.
Key Responsibilities
Lead the architecture of enterprise AI solutions supporting:
- Generative AI
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- AI model training
- Model fine-tuning
- AI inference
- Agentic AI
- Data science
- Machine learning
- Computer vision
- 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
- Hybrid AI environments
AI Architecture
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 Quantum InfiniBand
- NVIDIA BlueField 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.
AI Use Case Discovery
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
- Model requirements
- Model sizes
- Training requirements
- Inference requirements
- Dataset sizes
- Data sources
- RAG requirements
- Performance requirements
- Security and governance requirements
- Availability requirements
- Growth expectations
Translate use cases into an actionable AI architecture and technology roadmap.
AI Infrastructure Architecture
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
- NVIDIA Quantum InfiniBand
- RDMA/RoCE
- NVIDIA BlueField
- NVIDIA ConnectX
- GPUDirect
- High-performance Ethernet
- Leaf/spine architectures
Storage
Design AI storage platforms optimized for:
- Training datasets
- Model repositories
- Checkpointing
- RAG data
- High-performance parallel access
- AI data pipelines
- Long-term data retention
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.
AI Platform Architecture
Design the platform layer required to operate AI workloads at enterprise scale.
Solutions may incorporate:
- NVIDIA AI Enterprise
- NVIDIA NIM
- Kubernetes
- Red Hat OpenShift
- Slurm
- Containerized GPU environments
- MLOps platforms
- Model repositories
- API gateways
- Vector databases
- Data pipelines
- AI observability
- Infrastructure automation
Work with AI development teams to ensure infrastructure and platforms are optimized for the intended workloads.
Data & RAG Architecture
Assist customers in designing the data architecture supporting enterprise AI.
Provide architectural guidance around:
- Enterprise data sources
- Data ingestion
- Data pipelines
- Object and file storage
- Vector databases
- Embedding pipelines
- RAG architectures
- Model access to enterprise data
- Data security
- Data lifecycle management
Partner with application, data, and AI specialists where deeper application-level expertise is required.
AI Security & Governance
Incorporate security into AI solution architectures.
Address requirements including:
- Identity and access management
- Data security
- Network segmentation
- API security
- Model access
- Data privacy
- AI workload isolation
- Logging and monitoring
- AI governance
- Model governance
Work with cybersecurity architects to integrate AI environments into the customer's broader enterprise security architecture.
AI Factory & Data Center Architecture
Assess whether customer facilities can support high-density AI infrastructure.
Provide architectural guidance for:
- Rack density
- Power
- Cooling
- Liquid cooling
- Power distribution
- Network cabling
- Fiber infrastructure
- Rack layouts
- Resiliency
- Physical security
Identify when customer requirements are better served through colocation, hosted infrastructure, cloud, or GPU-as-a-Service alternatives.
Technology Partner Ecosystem
Maintain strong technical relationships with key AI infrastructure partners.
Work with NVIDIA-certified and approved ecosystem vendors across:
- Compute
- Networking