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About This Role
About Us
Since 1989, SHI International Corp. has helped organizations change the world through technology. We’ve grown every year since, and today we’re proud to be a $16 billion global provider of IT solutions and services.
Over 17,000 organizations worldwide rely on SHI’s concierge approach to help them solve what’s next. But the heartbeat of SHI is our employees – all 7,000 of them. If you join our team, you’ll enjoy:
- Our commitment to diversity, as the largest minority- and woman-owned enterprise in the U.S.
- Continuous professional growth and leadership opportunities.
- Health, wellness, and financial benefits to offer peace of mind to you and your family.
- World-class facilities and the technology you need to thrive – in our offices or yours.
Job Summary
We are seeking an experienced Artificial Intelligence (AI) Program Manager to lead and govern complex AI infrastructure, AI deployment, and AI transformation programs across the IDCS organization. This role serves as the strategic bridge between Sales, Pre-Sales, Engineering, Integration Services, Operations, Delivery, Cybersecurity, Datacenter, and internal and external customer stakeholders to ensure successful execution of AI-related initiatives while maintaining alignment with business objectives, operational readiness, and customer outcomes.
The AI Program Manager will drive end-to-end program management for AI infrastructure deployments, AI Labs initiatives, GPU-based solutions, AI field deployment programs, automation projects, and AI-driven operational transformation initiatives. The role requires a blend of program management leadership, technical acumen mainly in the AI, Cybersecurity, and Datacenter domains, change management expertise, executive communication skills, and cross-functional coordination capabilities.
Reporting to the Director of PMO, this role will be responsible for ensuring disciplined execution of strategic AI initiatives through robust governance, risk management, dependency oversight, resource coordination, and executive-level reporting. The individual will serve as a key enabler of successful program delivery, organizational alignment, and business value realization.
This position plays a critical role in enabling IDCS to scale AI delivery capabilities while ensuring execution excellence, customer satisfaction, operational efficiency, and business growth.
The AI Program Manager reports to the Director of IDCS PMO. This position is required to report to the SHI Piscataway, NJ office location as determined by SHI management with some travel. Role Description
Program Leadership & Governance
- Lead multiple concurrent AI programs and strategic initiatives across Datacenter, AI, and Cyber solution domains.
- Define program objectives, scope, schedules, milestones, dependencies, and success criteria.
- Establish and maintain governance structures, steering committees, executive reviews, and stakeholder communication plans.
- Develop program roadmaps aligned with IDCS strategic priorities and growth objectives.
- Ensure adherence to PMO standards, methodologies, processes, and reporting requirements.
- Facilitate executive decision-making through data-driven program reporting and recommendations.
AI Infrastructure & Deployment Management
- Manage AI infrastructure programs involving GPU clusters, AI factories, high-performance computing platforms, storage, networking, and field deployment initiatives.
- Provide thought management on emerging trends in rack-scale computing, AI infrastructure, liquid cooling, and next-generation data center design.
- Coordinate AI deployment readiness activities across engineering, logistics, integration, field services, operations, and internal and external customer teams.
- Oversee customer onboarding for strategic AI programs and ensure successful execution from concept through production.
- Establish white-glove engagement protocols that become the repeatable standard for onboarding future enterprise and hyperscale customers.
- Manage cross-functional dependency planning and execution across multiple workstreams.
- Ensure operational readiness for large-scale AI engagements and infrastructure implementations.
Strategic Program Management
- Under the guidance of the Director of IDCS PMO, drive the prioritization and governance of AI/Cyber product backlogs to balance business value, strategic alignment, resource availability, and risk.
- Develop portfolio-level visibility into AI programs, resource planning/utilization, demand/capacity analysis, and business outcomes.
- Maintain executive dashboards and KPI reporting for leadership teams.
Cross-Functional Leadership
- Collaborate with:
+ Sales, Pre-Sales, and Business Development + Solutions Architects + Engineering + Integration Center Operations + Delivery Teams + AI/Cybersecurity Teams + Product Management + Leadership
- Facilitate alignment across technical and business stakeholders.
- Resolve conflicts, remove roadblocks, and drive accountability across programs.
Risk, Change & Issue Management
- Identify and proactively manage program risks, assumptions, constraints, and dependencies.
- Lead escalation management and executive communications for high-priority issues.
- Establish change management practices and governance controls for AI programs.
- Ensure mitigation strategies are implemented and tracked to closure.
Resource & Capacity Planning
- Coordinate resource demand planning across AI project portfolios.
- Forecast staffing needs and identify capability gaps.
- Support workforce planning and bench-strength development initiatives.
- Contribute to the PMO's strategic vision to improve organizational readiness for future AI growth.
AI Enablement & Continuous Improvement
- Support AI training, certification, and capability development programs.
- Drive adoption of best practices, standards, automation opportunities, and operational efficiencies.
- Identify opportunities to leverage AI technologies to improve delivery performance and customer experience.
- Champion continuous improvement initiatives focused on scalability, quality, and delivery excellence.
Customer Onboarding & Executive Engagement
- Own the end-to-end technical onboarding experience for large-scale strategic customers, from initial engagement through production handoff.
- Lead design reviews, solution workshops, and architecture sessions with customer and internal technical teams.
- Serve as the escalation point as required and trusted advisor for customers during onboarding, ensuring commitments are met and expectations are exceeded.
- Proactively identify risks, dependencies, and technical blockers and drive cross-functional resolution with urgency.
- Advise customers on best practices and implementation considerations, ensuring transparent alignment between customer expectations and SHI delivery capabilities prior to execution.
Behaviors and Competencies
- Communication: Can effectively communicate complex ideas and information to diverse audiences, facilitate effective communication between others, and mentor others in effective communication.
- Interpersonal Skills: Can communicate effectively, build relationships, resolve conflicts, influence others, and support others in developing their interpersonal skills in major situations.
- Follow-Up: Can take ownership of tasks, collaborate with others in managing follow-ups, and drive results through effective task completion.
- Collaboration: Can take ownership of team initiatives, foster a collaborative environment, and ensure that all team members feel valued and heard.
- Problem-Solving: Can proactively identify and take ownership of complex problem-solving initiatives, initiate preventative measures, collaborate with others to find solutions, and drive successful outcomes.
- Time Management: Can manage time effectively, accurately estimate the time required for specific tasks, balance multiple tasks, and help others in improving their time management skills.
- Organization: Can oversee complex projects with multiple moving parts, ensure team alignment with organizational systems, and adapt to changing priorities.
- Digital Acumen: Can take ownership of complex digital initiatives, collaborate with others, and drive digital results.
- Leadership: Can take ownership of complex team initiatives, collaborate with others in decision-making processes, and drive team performance.
Skill Level Requirements
Education
- Bachelor’s degree in Information Technology, Engineering, Computer Science, Business, or related field.
- Master’s degree preferred.
Experience
- 10+ years of progressive Program Management experience.
- 7+ years managing complex technology programs.
- Experience leading cross-functional enterprise initiatives.
- Experience with AI, Cyber, datacenter, infrastructure, cloud, or digital transformation programs.
- Domain understanding of AI/ML infrastructure, including GPU compute platforms, high-performance networking, and advanced cooling solutions.
- Proven success managing executive-level stakeholders and strategic customer engagements.
- Exceptional communication, presentation, and cross-functional collaboration skills.
Technical Knowledge
Preferred experience in one or more of the following:
- AI Infrastructure
- GPU Platforms (NVIDIA ecosystem preferred)
- Datacenter Solutions
- High-Performance Computing (HPC)
- Hybrid Cloud Platforms
- Infrastructure Modernization
- Automation & Orchestration
- Digital Transformation Programs
- Cybersecurity Programs
Preferred Certifications
- PMP (Project Management Professional)
- PMI-PgMP (Program Management Professional)
- PMI-CPMAI (Program Management in Managing AI)
- Certified AI Program Manager (CAIPM)
- NVIDIA AI Certifications
- Cloud Certifications (Azure, AWS, GCP)
Other Requirements
- Required to report to the SHI Piscataway, NJ office location as determined by SHI management.
- Some travel required.
- Ability to work across highly matrixed technical and business organizations.
- Ability to support large-scale AI infrastructure deployments, enterprise customer onboarding, and AI transformation initiatives.
- Ability to manage multiple concurrent strategic programs while maintaining governance, operational readiness, and customer satisfaction.
The estimated annual pay range for this position is $160,000 - $220,000 which includes a base salary and bonus. The compensation for this position is dependent on job-related knowledge, skills, experience, and market location and, therefore, will vary from individual to individual. Benefits may include, but are not limited to, medical, vision, dental, 401K, and flexible spending.
Equal Employment Opportunity – M/F/Disability/Protected Veteran Status
Salary Context
This $160K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At SHI International, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $160K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
SHI International AI Hiring
SHI International has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Piscataway, NJ, US, Remote, US, Somerset, NJ, US. Compensation range: $130K - $325K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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