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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
The Director - AI Sales & Solutions leads SHI's AI opportunity development organization, responsible for developing innovative, customer-centric AI solutions and transforming market interest into qualified, net-new AI pipeline across every SHI Business Unit. Reporting within Advanced Growth Technologies (AGT), this role leads a team of AI Advisors who collaborate directly with SHI sellers to identify, qualify, and accelerate AI opportunities spanning Public Sector, Global, Enterprise, and Commercial Sales.
This position is responsible for designing and executing AI growth strategies that align with organizational objectives and evolving market demands. The Director develops repeatable pursuit methodologies, aligns internal and partner resources, manages AI solution portfolios, and ensures the successful delivery of AI initiatives across the Full Stack AI market, including Applications, Models, Infrastructure, Chips, Energy, Security, and Modern Workplace solutions.
The Director builds and maintains strategic relationships with sales teams, business units, customers, technology partners, and executive stakeholders while fostering a culture of innovation, operational excellence, customer focus, and continuous improvement. Success in this role is measured through qualified pipeline growth, pursuit velocity, conversion rates, revenue contribution, gross margin impact, and overall AI business expansion. Role Description
- Lead the AI Sales & Solutions organization in developing innovative, customer-centric AI solutions that align with organizational goals and market demands.
- Refine, develop, and scale a high-performing team of AI Advisors responsible for AI opportunity identification, qualification, and pursuit development.
- Collaborate with sales, business development, product, and technical teams to identify customer needs and design tailored AI solutions that drive business growth.
- Establish and operationalize repeatable AI sales motions and pursuit methodologies utilizing proven qualification frameworks and sales practices.
- Oversee the development and deployment of AI solution architectures, ensuring scalability, security, performance, and alignment with customer requirements.
- Manage the AI solutions portfolio, ensuring alignment with business objectives, market opportunities, and emerging technology trends.
- Drive net-new AI pipeline generation through targeted engagement strategies tailored to Public Sector, Enterprise, Global, and Commercial business units.
- Partner with Business Development teams to identify target initiatives, prioritize strategic accounts, align source campaigns, and improve AI sales readiness.
- Coordinate AGT resources, Enterprise Architects, AI Infrastructure specialists, AI & Cyber Labs, and strategic technology partners to support customer pursuits and solution delivery.
- Build and maintain strong relationships with customers, partners, executive leadership, and internal teams to ensure successful AI solution outcomes.
- Align NVIDIA, OEM, cloud, ISV, and other strategic partners within active pursuits to deliver differentiated customer solutions.
- Drive continuous improvement in AI solutions development by leveraging emerging technologies, methodologies, and industry best practices.
- Lead efforts in organizational change management to ensure successful adoption of AI technologies, solutions, and sales motions.
- Ensure compliance with legal, regulatory, security, and organizational standards across all AI solution activities.
- Develop and execute AI Advisor certification programs, coaching strategies, and career development initiatives.
- Monitor performance metrics, pipeline health, conversion rates, revenue performance, and customer outcomes.
- Prepare and present executive-level metrics, scorecards, and business reviews, providing strategic insights and recommendations for organizational decision-making.
- Serve as a visible ambassador of SHI's AI strategy through executive briefings, customer engagements, partner interlocks, industry events, and strategic planning initiatives.
Mentor and develop team members while fostering a culture of innovation, accountability, collaboration, and customer focus. *
The Director - AI Sales & Solutions strategic business impact will include:
- Refine and scale a world-class team of AI Advisors.
- Accelerate net-new AI opportunity identification and qualification.
- Create repeatable AI pursuit and qualification methodologies.
- Increase seller readiness and AI sales effectiveness.
- Align AGT resources and strategic partners to maximize win rates.
- Drive adoption of Full Stack AI solutions across SHI's customer base.
Deliver measurable growth in qualified pipeline, revenue, profitability, and customer outcomes. *
Behaviors and Competencies
- Leadership: Leads strategic organizational initiatives, inspires others to assume leadership responsibilities, develops high-performing teams, and fosters a culture of accountability, innovation, and continuous improvement.
- Strategic Thinking: Evaluates complex market opportunities, anticipates industry trends, develops long-term AI growth strategies, and drives organizational transformation through effective execution.
- Business Acumen: Provides strategic guidance and insight regarding AI market trends, customer investment priorities, ROI, TCO, and business growth opportunities.
- Communication: Models exceptional communication with executive leadership, customers, partners, and internal stakeholders; presents effectively across large audiences and influences strategic decision making.
- Change Management: Leads organizational adoption of AI technologies, develops and implements change strategies, and facilitates successful transformation initiatives.
- Collaboration: Builds strong cross-functional partnerships, aligns diverse stakeholder groups, and fosters a culture of shared purpose and collective success.
- Customer-Centric Mindset: Champions customer-focused innovation by understanding customer challenges and aligning AI solutions to measurable business outcomes.
- Problem-Solving: Leads strategic problem-solving initiatives, addresses complex business challenges, and drives proactive solutions that improve organizational performance.
- Results Orientation: Establishes ambitious goals, drives execution excellence, monitors outcomes, and holds teams accountable for achieving measurable business results.
- Strategic Implementation: Develops and executes comprehensive business plans, operational frameworks, and go-to-market strategies to achieve AI growth objectives.
Creativity and Innovation: Drives breakthrough thinking, encourages experimentation, and fosters a culture that embraces emerging technologies and innovative solutions. *
Skill Level Requirements
- Ability to develop and execute AI solutions strategies that align with overall business objectives and long-term growth initiatives. - Expert
- Demonstrated success leading pipeline generation, sales acceleration, and opportunity qualification organizations. - Expert
- Skilled in engaging executive stakeholders, customers, partners, and internal teams to drive successful business outcomes. - Expert
- Deep understanding of AI technologies, Full Stack AI ecosystems, and modern technology solution architectures. - Expert
- Expertise in managing and executing technical and business initiatives related to AI solutions development and deployment. - Expert
- Skilled in assessing and mitigating risks associated with AI adoption, solution implementation, and business transformation. - Expert
- Ability to lead organizational change while maintaining team engagement, productivity, and alignment. - Expert
- Skilled in understanding customer business challenges and aligning AI solutions to drive satisfaction, adoption, and revenue growth. - Expert
- Demonstrated ability to present to executive audiences and communicate business value through ROI, TCO, and strategic outcomes. - Expert
Experience managing partner ecosystems including cloud providers, OEMs, ISVs, and AI technology partners. - Expert *
Other Requirements
- Completed Bachelor's Degree in Information Technology, Computer Science, Business, Engineering, or a related field, or equivalent relevant work experience required.
- 7-10 years of experience in Information Technology, AI, Sales Engineering, Solutions Architecture, Business Development, or a related functional area.
- 4+ years of leadership and people management experience required.
- Demonstrated experience leading technical, solutions, sales engineering, business development, or AI-focused teams.
- Strong understanding of Artificial Intelligence technologies, industry trends, and go-to-market strategies.
- Ability to travel to SHI, partner, customer, and industry events.
- Ability to travel up to 25%.
The estimated annual pay range for this position is $175,000 - $230,000 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 $175K-$230K 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,250. Disclosed range: $175K to $230K.
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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