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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 AI Solution Advisor serves as a dedicated AI opportunity development specialist responsible for helping SHI sellers identify, qualify, and accelerate net-new AI opportunities across Public Sector, Global Sales, Enterprise Sales, Commercial Sales, and all other SHI Business Units.
The AI Solution Advisor acts as an extension of the seller, helping customers connect business challenges to practical AI outcomes while navigating the rapidly evolving Full Stack AI ecosystem. The role combines business discovery, strategic account planning, AI solution positioning, pursuit development, and resource orchestration to create qualified pipeline and accelerate revenue growth.
The AI Solution Advisor is not expected to be the deepest technical expert within SHI. Instead, they serve as the primary opportunity quarterback, engaging the appropriate AGT specialists, Enterprise Architects, AI Infrastructure teams, AI & Cyber Labs, Services teams, and strategic partners to move AI opportunities forward.
Success is measured through account coverage, seller engagement, qualified pipeline creation, opportunity progression, conversion rates, and overall contribution to SHI's AI growth objectives.
This position is based on New York, New Jersey, Los Angeles, or San Francisco, California. Role Description
Drive Net-New AI Pipeline Generation
- Partner directly with Account Executives, Sales Leaders, and Business Unit stakeholders to identify, develop, and qualify AI opportunities.
- Conduct customer discovery conversations focused on business outcomes, operational challenges, and AI readiness.
- Apply qualification frameworks such as BANTC and MEDDIC to determine opportunity viability.
- Create and maintain a healthy pipeline of qualified AI opportunities within assigned territories and business units.
- Assist in converting marketing-generated leads, partner referrals, and seller-identified opportunities into active pursuits.
Support AI-Centric Target Initiatives
- Help sellers position AI opportunities aligned to customer priorities and Target Initiatives and subsequent Source Campaigns.
- Tailor AI sales motions for Commercial, Enterprise, Global, Public Sector, and all other SHI Business Unit customers.
- Work closely with both Sales and AGT's Business Development Teams to take action on AI-specific account plans and follow up on Executive Briefing Center (EBC) engagements.
Improve Seller Readiness & AI Awareness
- Educate sellers on SHI's AI portfolio, capabilities, and GTM programs.
- Deliver AI opportunity identification coaching and guidance.
- Share AI use cases, industry trends, and best practices with account teams.
- Help prepare sellers for customer conversations involving AI strategies and initiatives.
- Promote adoption of AI account-planning methodologies and prompting frameworks.
Coordinate AGT & Technical Resources
- Ensure the right SHI resources are aligned at the appropriate stage of the sales cycle.
- Facilitate engagement with Enterprise Architects, AI Solution Architects, Infrastructure Specialists, AI Labs, Cyber Labs, and Services teams.
- Support solution validation and opportunity advancement activities.
CRM, Reporting & Pipeline Management
- Maintain opportunity documentation and qualification details within CRM.
- Track opportunity progression and next-step actions.
- Participate in regular pipeline reviews and forecasting discussions.
- Deliver accurate reporting related to activity levels, opportunity status, and revenue potential.
- Contribute data and insights that improve pursuit methodologies and overall sales effectiveness.
Behaviors and Competencies
- Relationship Building: Can proactively seek out opportunities to expand networks, initiate collaborations, and contribute to team cohesion.
- Analytical Thinking: Can synthesize complex data, identify patterns, draw insights, and present findings clearly and understandably.
- Collaboration: Can proactively seek out diverse perspectives, facilitate open communication among team members, and drive toward consensus and action.
- Strategic Thinking: Can analyze situations and can lead the development and execution of strategic initiatives.
- Self-Motivation: Can proactively seek out challenges, initiate self-development projects, and contribute to personal or professional innovative ideas.
- Communication: Can effectively communicate complex ideas and information to diverse audiences and can facilitate effective communication between others.
- Time Management: Can consistently use time effectively, balance multiple tasks, and meet deadlines.
- Organization: Can effectively coordinate multiple projects, delegate tasks where appropriate, and employ advanced organizational tools and methods.
- Decision-Making: Can analyze complex information, predict long-term consequences, and make decisions that align with strategic goals.
- Training: Can proactively seek out areas of improvement, initiate training programs, and contribute to innovative learning methods.
Skill Level Requirements
- 3-7+ years of experience in technology sales, business development, solution advisory, or technical presales.
- Experience supporting complex technology or digital transformation initiatives.
- Proven success in identifying and qualifying new business opportunities.
- Experience working with enterprise customers and executive stakeholders.
Preferred Qualifications
- Experience selling AI, cloud, infrastructure, cybersecurity, analytics, or data solutions.
- Familiarity with NVIDIA, Dell Technologies, Cisco, Lenovo, or leading AI partners.
- Exposure to enterprise AI initiatives and generative AI technologies.
- AI-related certifications or willingness to pursue AI certifications.
- Experience supporting executive workshops, strategy sessions, or briefing centers.
Other Requirements
Business Skills
- Strong discovery and consultative selling skills.
- Ability to translate business problems into technology solutions.
- Effective communication and presentation abilities.
- Strong organizational and opportunity management skills.
- Ability to manage multiple pursuits simultaneously.
AI & Technology Knowledge
- Foundational understanding of artificial intelligence technologies and market trends.
- Knowledge of cloud, infrastructure, security, data, or application ecosystems.
- Interest in staying current on emerging AI platforms, vendors, and use cases.
- Familiarity with modern sales methodologies and opportunity qualification frameworks.
The estimated annual pay range for this position is $125,000 - $150,000 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 $125K-$150K range is in the lower quartile 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $125K to $150K.
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.
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 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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