AI Architect Jobs in Arizona
AI Architect jobs in Arizona are in strong demand, with active hiring concentrated in financial services, defense and aerospace contracting, semiconductor manufacturing, and healthcare technology across the Phoenix metropolitan area, Scottsdale, and Tucson. Companies with a lasting Arizona footprint such as Intel, Banner Health, and Raytheon Technologies regularly seek ai architects at both mid-career and senior levels to lead enterprise AI strategy, large language model deployment, and cloud-native ML infrastructure. The most in-demand specialties are generative AI integration, MLOps pipeline design, and responsible AI governance. Find a role that fits below and apply directly.
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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 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
- Storage
- Data center infrastructure
- AI software
- Data management
- Security
- Cloud
- Colocation
- Managed services
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.
Presales & Customer Engagement
Serve as the technical lead for strategic AI opportunities.
Responsibilities include:
- Customer discovery
- AI strategy workshops
- Technical presentations
- Whiteboarding
- Architecture development
- Solution sizing
- Bill of Materials development
- Proof-of-concept planning
- RFP/RFI responses
- Technical proposals
- Statements of Work
- Executive briefings
- Technical demonstrations
Communicate complex AI concepts effectively to audiences ranging from engineers and data scientists to CIOs and business executives.
Architecture Documentation
Produce professional customer-facing deliverables including:
- AI Solution Architectures
- High-Level Designs
- Logical architecture diagrams
- Physical architecture diagrams
- AI Factory designs
- Network topology diagrams
- Storage architectures
- GPU cluster architectures
- Bills of Materials
- Capacity models
- AI readiness assessments
- Implementation roadmaps
- Architecture Decision Documents
Ensure solutions can transition successfully from presales architecture into implementation.
Required Experience
- 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.
Preferred Technical Experience
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/Data: RAG architectures, vector databases, data pipelines, MLOps, model repositories and enterprise AI platforms.
Automation: Ansible, Terraform and infrastructure orchestration technologies.
Preferred Certifications
Relevant certifications may include:
- NVIDIA AI certifications
- NVIDIA AI Infrastructure certifications
- NVIDIA networking certifications
- Kubernetes certifications
- Red Hat certifications
- Cisco CCNP/CCIE
- Dell, DDN, Arista, NetApp, Pure Storage, or other relevant OEM certifications
Equivalent hands-on AI architecture experience may substitute for formal certifications.
Insight Investments, Corp is an Equal Employment Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, age, sexual orientation, gender identity, disability, status as a Veteran or other protected classification.
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 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
- Storage
- Data center infrastructure
- AI software
- Data management
- Security
- Cloud
- Colocation
- Managed services
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.
Presales & Customer Engagement
Serve as the technical lead for strategic AI opportunities.
Responsibilities include:
- Customer discovery
- AI strategy workshops
- Technical presentations
- Whiteboarding
- Architecture development
- Solution sizing
- Bill of Materials development
- Proof-of-concept planning
- RFP/RFI responses
- Technical proposals
- Statements of Work
- Executive briefings
- Technical demonstrations
Communicate complex AI concepts effectively to audiences ranging from engineers and data scientists to CIOs and business executives.
Architecture Documentation
Produce professional customer-facing deliverables including:
- AI Solution Architectures
- High-Level Designs
- Logical architecture diagrams
- Physical architecture diagrams
- AI Factory designs
- Network topology diagrams
- Storage architectures
- GPU cluster architectures
- Bills of Materials
- Capacity models
- AI readiness assessments
- Implementation roadmaps
- Architecture Decision Documents
Ensure solutions can transition successfully from presales architecture into implementation.
Required Experience
- 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.
Preferred Technical Experience
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/Data: RAG architectures, vector databases, data pipelines, MLOps, model repositories and enterprise AI platforms.
Automation: Ansible, Terraform and infrastructure orchestration technologies.
Preferred Certifications
Relevant certifications may include:
- NVIDIA AI certifications
- NVIDIA AI Infrastructure certifications
- NVIDIA networking certifications
- Kubernetes certifications
- Red Hat certifications
- Cisco CCNP/CCIE
- Dell, DDN, Arista, NetApp, Pure Storage, or other relevant OEM certifications
Equivalent hands-on AI architecture experience may substitute for formal certifications.
Insight Investments, Corp is an Equal Employment Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, age, sexual orientation, gender identity, disability, status as a Veteran or other protected classification.
See All 10 AI Architect Jobs in Arizona
Find roles in Arizona that match your experience and apply in just a few clicks.
Find AI Architect JobsAI Architect Jobs by City in Arizona
Where Arizona roles are concentrated, by current openings.
AI Architect Job Market in Arizona
A snapshot from current Arizona openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Banking & Financial Services
What Arizona Employers Look For
The qualifications that appear most often in AI architect jobs across Arizona.
- Bachelor's or master's degree in computer science, AI, data science, or a related field
- Demonstrated experience designing and deploying machine learning or deep learning systems at enterprise scale
- Proficiency with major cloud platforms such as AWS, Azure, or Google Cloud and their AI and ML services
- Hands-on experience with large language models, generative AI frameworks, and MLOps tooling
- Ability to translate complex AI capabilities into clear technical roadmaps for business stakeholders
- Familiarity with responsible AI principles, model governance, and regulatory compliance requirements
AI Architect Jobs in Arizona: Frequently Asked Questions
How do you become a ai architect in Arizona?
AI architect roles in Arizona do not require a state-issued license, so the path runs through education and credentials. Most Arizona employers expect at least a bachelor's degree in computer science, data science, or engineering, with a master's preferred for senior roles. Vendor certifications from AWS, Microsoft Azure, or Google Cloud carry significant weight in the Arizona market, where cloud-heavy industries like finance, defense, and healthcare dominate hiring. Building a portfolio of deployed ML systems is the most direct way to qualify.
How much do AI architects make in Arizona?
AI architects in Arizona earn a median of about $105,240 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $52,450 for the lowest 10% to over $166,870 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire ai architects in Arizona?
Employers hiring ai architects in Arizona right now include Viasat, Infosys, and RRA Capital, based on current listings on Migrate Mate as of November 2026. Arizona's mix of financial services headquarters, defense contractors, semiconductor firms, and large hospital systems means demand for ai architects is spread across several industries rather than concentrated in a single sector.
Which Arizona cities have the most ai architect jobs?
Phoenix, Tempe, and Scottsdale account for the largest share of ai architect openings in Arizona. The Phoenix and Scottsdale corridor drives the majority of postings, anchored by major financial institutions, healthcare networks, and technology companies headquartered or regionally based there, while Tucson's openings are shaped by defense and aerospace contractors with substantial engineering operations in the area.
Are there remote ai architect jobs in Arizona?
Yes, and more than most fields. About 75% of ai architect openings tied to Arizona are remote or hybrid as of November 2026, reflecting how much of this work involves design, modeling, and code rather than on-site infrastructure. The most remote-friendly parts of the role include model development, architecture documentation, and stakeholder consultation, while roles requiring hands-on integration with on-premises systems or regulated data environments tend to require at least partial on-site presence.
How can I get hired as a ai architect in Arizona with little or no experience?
The most realistic entry point is a machine learning engineer or data scientist role at a large Arizona employer, then building toward architecture responsibilities from there. Companies like Intel and Banner Health run graduate hiring programs and rotational technology tracks that bring in candidates without deep enterprise AI experience. Completing a recognized cloud certification, contributing to an open-source AI project, or completing a graduate program at Arizona State University strengthens an early-career application considerably and is a credential Arizona hiring managers in tech and healthcare explicitly look for.
Where can I find and apply to ai architect jobs in Arizona?
You can find and apply to ai architect jobs in Arizona on Migrate Mate, which lists current Arizona openings from employers actively hiring in the state. Search the listings, find roles that match your background and target location, and apply directly to the ones that fit.
See All 10 AI Architect Jobs in Arizona
Find roles in Arizona that match your experience and apply in just a few clicks.
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