Principal AI Architect

Government Acquisitions; United States; On-site; USD 180,000 - 280,000; Full time

Job summary

Government Acquisitions in the United States seeks a Principal AI Infrastructure Architect to serve as the senior technical authority for AI infrastructure, accelerated computing, and enterprise AI platform design. You will lead architectures for AI factories, GPU-based compute, and large-scale AI environments for Federal customers.

Reporting to the CTO, this role shapes strategy, reference architectures, and capacity planning while partnering with NVIDIA, OEMs, and ecosystem partners to deliver

Qualifications

• 15+ years of experience in enterprise infrastructure, data center architecture, cloud, HPC, or related domains.
• Demonstrated experience designing complex, large-scale AI, HPC, GPU, or accelerated computing environments.
• Deep technical knowledge of GPU infrastructure, distributed computing, high-performance storage, networking, data platforms, and hybrid cloud architectures.
• Experience in secure, resilient, and scalable AI training, inference, LLM, RAG, or agentic AI workloads.

Responsibilities

• Serve as the senior technical authority for AI infrastructure, accelerated computing, GPU architectures, and enterprise AI platform design.
• Lead architecture and strategy for AI factories and large-scale AI environments including compute, storage, networking, and security.
• Develop reusable reference architectures, sizing methodologies, capacity models, and implementation frameworks for Federal AI deployments.
• Design and validate GPU-based platforms for AI training and inference, including cluster architecture and lifecycle planning.
• Provide technical leadership across NVIDIA ecosystem partnerships and joint solution development.
• Architect high-performance storage and data solutions for AI workloads and data lakes.
• Define infrastructure patterns for enterprise LLM and generative AI deployments, including secure model hosting and RAG.

Skills

Executive communication

Federal Gov experience

Security architecture

GPU infrastructure

Distributed computing

HPC

Tools

NVIDIA DGX

NVIDIA HGX

CUDA

NVIDIA AI Enterprise

NIM microservices

AI Factory

Job description

Government Acquisitions, Inc. (GAI), a Computacenter company, is seeking a highly accomplished Principal AI Infrastructure Architect to serve as the company’s senior technical authority for AI infrastructure, accelerated computing, and enterprise AI platform architecture. This role leads the technical strategy and design of AI factories, GPU-based computing environments, large language model (LLM) platforms, and next-generation AI infrastructure for U.S. Federal Government customers. The Principal Architect works directly with executive leadership, sales, solution engineering, strategic technology partners, and customers to translate complex technical requirements into secure, scalable, mission-aligned solutions. Reports to the CTO.

Key Responsibilities

• Serve as GAI’s senior technical authority for AI infrastructure, accelerated computing, GPU architectures, and enterprise AI platform design.
• Lead architecture and technical strategy for AI factories and large-scale AI environments spanning compute, storage, networking, data platforms, security, and AI software.
• Develop and maintain reusable reference architectures, design standards, sizing methodologies, capacity models, and implementation frameworks for Federal AI deployments.
• Design and validate GPU-based platforms for AI training and inference, including cluster architecture, workload placement, performance optimization, scalability, resiliency, and lifecycle planning.
• Provide technical leadership across the NVIDIA ecosystem, including DGX, HGX, NVIDIA AI Enterprise, NIM microservices, CUDA, and AI Factory architectures; support joint workshops, solution development, and go-to-market activities with NVIDIA and ecosystem partners.
• Architect high-performance storage and data solutions for AI training, inference, data lakes, hybrid AI environments, and other data-intensive workloads; evaluate technologies based on performance, scale, availability, security, and operational requirements.
• Design high-performance networking for distributed AI environments, including GPU-to-GPU communication, cluster fabrics, distributed training, large-scale inference, and AI Factory deployments.
• Define infrastructure patterns for enterprise LLM and generative AI deployments, including model hosting and serving, Retrieval-Augmented Generation (RAG), agentic AI, secure model access, and Federal on-premises, hybrid, and air-gapped environments.
• Provide technical leadership for GAI’s most strategic AI pursuits by supporting customer discovery, executive briefings, architecture workshops, capture activities, proposals, competitive positioning, solution reviews, and technical win strategies.
• Partner with Pre-Sales Solution Engineering to qualify opportunities, review complex architectures, mentor technical staff, and ensure solution quality, consistency, and technical excellence.
• Evaluate emerging AI infrastructure technologies and advise executive leadership on technical strategy, partner priorities, investments, Innovation Lab capabilities, and differentiated solution offerings.
• Build trusted-advisor relationships with Federal customers and maintain strong technical relationships with OEM, ISV, cloud, data center, and ecosystem partner leadership.

Qualifications & Experience

• 15+ years of experience in enterprise infrastructure, data center architecture, cloud, high-performance computing (HPC), or related technical domains, including 7+ years in senior architecture or technical leadership roles.
• Demonstrated experience designing complex, large-scale AI, HPC, GPU, or accelerated computing environments.
• Deep technical knowledge of GPU infrastructure, distributed computing, high-performance storage, networking, enterprise data platforms, and hybrid cloud architectures.
• Experience designing secure, resilient, and scalable infrastructure for AI training, inference, generative AI, LLM, RAG, or agentic AI workloads.
• Proven ability to lead complex technical engagements with executive-level customers and translate mission and business requirements into actionable architectures.
• Strong communication, presentation, documentation, and customer-facing skills, with the ability to explain advanced technical concepts to both technical and non-technical audiences.
• Experience supporting U.S. Federal Government customers and familiarity with Federal security, compliance, acquisition, and mission requirements.
• Ability and willingness to travel for customer engagements, technical workshops, partner activities, and strategic pursuits as required.

Preferred Qualifications

• Hands-on architecture experience with NVIDIA DGX, HGX, NVIDIA AI Enterprise, NIM, CUDA, and AI Factory solutions.
• Experience supporting Department of Defense, Intelligence Community, and/or Civilian Federal agencies.
• Experience architecting solutions using technologies from NVIDIA, Dell Technologies, HPE, Cisco, VAST Data, WEKA, Pure Storage, NetApp, Microsoft Azure, AWS, or comparable enterprise platforms.
• Experience with enterprise generative AI, machine learning, LLM serving, RAG, containerized AI platforms, Kubernetes, and model lifecycle infrastructure.
• Active U.S. Government security clearance or ability to obtain and maintain a clearance.

Measures of Success

Quality, scalability, security, and repeatability of AI infrastructure architectures; adoption of reference designs and technical standards; customer and executive confidence in GAI’s technical leadership; effectiveness of strategic pursuit and proposal support; technical win contribution and AI infrastructure pipeline influence; development and mentorship of engineering teams; and strength of joint solution development with NVIDIA and other strategic partners.

Primary Outcome

Establish GAI as a recognized technical leader in Federal AI infrastructure by translating mission requirements into secure, high-performance, multi-vendor AI architectures that accelerate customer outcomes, strengthen strategic partnerships, improve technical win rates, and enable successful deployment of enterprise AI at scale.