AI Solutions Architect
insight investmentsHouston (TX)
AI Solutions Architect
Posted today
insight investmentsHouston (TX)
SENIORITY
Lead
About the role
Job Description
Position 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 AILarge Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)AI model training
- Model fine-tuningAI inference
- Agentic AIData 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 infrastructureNVIDIA DGX-based infrastructureNVIDIA DGX SuperPODNVIDIA-certified OEM infrastructure
ColocationPrivate cloud
Public cloudGPU-as-a-Service
Hybrid AI environmentsAI Architecture
Develop AI solutions aligned with NVIDIA reference architectures and validated designs.
Architect solutions incorporating technologies such as or equivalent to:
NVIDIA DGXNVIDIA DGX SuperPODNVIDIA HGXNVIDIA GB200/GB300 NVL systemsNVIDIA H100/H200 and Blackwell-generation GPUsNVIDIA NVLink and NVLink SwitchNVIDIA Spectrum-XNVIDIA Quantum InfiniBandNVIDIA BlueField DPUsNVIDIA ConnectXNVIDIA AI EnterpriseNVIDIA NIMNVIDIA CUDANVIDIA NCCLNVIDIA Base CommandNVIDIA GPU OperatorNVIDIA 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 objectivesAI use cases
Model requirements
Model sizes
Training requirements
Inference requirements
Dataset sizes
Data sourcesRAG requirements
Performance requirements
Security and governance requirements
Availability requirements
Growth expectations
Translate use cases into an actionable AI architecture and technology roadmap.
AI Infrastructure ArchitectureDesign 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 EthernetNVIDIA Quantum InfiniBandRDMA/RoCENVIDIA BlueFieldNVIDIA ConnectXGPUDirectHigh-performance Ethernet
Leaf/spine architectures
StorageDesign AI storage platforms optimized for:
Training datasets
Model repositories
CheckpointingRAG data
High-performance parallel accessAI 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 ArchitectureDesign the platform layer required to operate AI workloads at enterprise scale.
Solutions may incorporate:
NVIDIA AI EnterpriseNVIDIA NIMKubernetesRed Hat OpenShiftSlurmContainerized GPU environmentsMLOps platforms
Model repositoriesAPI gateways
Vector databases
Data pipelinesAI 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 pipelinesRAG 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 segmentationAPI security
Model access
Data privacyAI workload isolation
Logging and monitoringAI 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
PowerCoolingLiquid cooling
Power distribution
Network cabling
Fiber infrastructure
Rack layouts
ResiliencyPhysical 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
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