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About This Role
Principal AI Infrastructure Architect Role Summary Government Acquisitions, LLC (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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Government Acquisitions, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Government Acquisitions, Inc. AI Hiring
Government Acquisitions, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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