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About This Role
About Us
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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
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The Solutions Engineer for the AI Lab will support the development, evaluation, and delivery of AI\-focused infrastructure and platform solutions across Advanced Growth Technologies. This role serves as a technical resource for AI solutions, helping translate customer and business requirements into validated architectures, infrastructure designs, Bills of Materials (BOMs), and deployment recommendations.
This position partners closely with AI Lab leadership, Solutions Architects, sales teams, engineering, and technology partners to support solution development, technical validation, customer engagements, and presales activities. The role is focused on executing and enabling AI Lab solutions through technical expertise, solution configuration, technical demonstrations, and customer\-facing support.
Role Description
Solution Development \& Validation
- Design and validate AI infrastructure solutions that align with customer use cases, AI Lab offerings, partner technologies, and business requirements.
Utilize OEM and partner reference architectures to recommend scalable, performant, and cost\-effective AI platforms.
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Customer \& Technical Engagement
- Participate in customer discovery sessions, workshops, and technical consultations to assess requirements and recommend AI infrastructure solutions.
Act as a technical advisor throughout the presales lifecycle, helping customers understand solution capabilities, deployment considerations, and expected outcomes.
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Configuration \& BOM Development
- Develop detailed infrastructure configurations including servers, GPUs, networking, storage, power, cooling, and rack requirements.
Create and maintain accurate Bills of Materials (BOMs), sizing recommendations, and technical solution documentation.
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Proof of Concept \& Demonstrations
- Support and deliver technical demonstrations, solution walkthroughs, proofs of concept (POCs), and platform validation activities for customers and internal stakeholders.
Assist with testing and validating AI infrastructure designs to ensure readiness for customer deployment.
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Cross\-Functional Collaboration
- Partner with AI Lab leadership, Solutions Architects, Sales, Engineering, Product Management, and Partner teams to support solution development and customer opportunities.
Collaborate with account teams to align technical recommendations with customer business objectives and desired outcomes.
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Partner \& Technology Alignment
- Work closely with NVIDIA, OEM partners, cloud providers, and technology vendors to stay current on product roadmaps, certifications, and emerging AI technologies.
Support evaluation of AI platforms, infrastructure components, and solution patterns for customer and AI Lab use cases.
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Technical Enablement
- Assist in developing technical enablement materials, solution documentation, playbooks, and training content for internal sales and engineering teams.
Help educate SHI teams on AI infrastructure trends, technologies, and best practices.
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Pipeline \& Opportunity Support
- Support opportunity qualification, solution scoping, technical proposal development, and responses to RFPs, RFIs, and customer technical inquiries.
Assist account teams in identifying opportunities across AI, Data Center, Cloud, Network, Security, and Services solution areas.
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Behaviors and Competencies
- Presenting: Can design and deliver engaging presentations, adapting the content and style to suit the audience, context, and medium.
- Negotiation: Can proactively seek out negotiation opportunities, initiate discussions, and contribute to conflict resolution.
- Communication: Can effectively communicate complex ideas and information to diverse audiences and can facilitate effective communication between others.
- Detail\-Oriented: Can manage complex tasks or projects, identifying errors or inconsistencies, and ensuring all details are addressed, necessary corrections are made, and quality is maintained.
- Organization: Can effectively coordinate multiple projects, delegate tasks where appropriate, and employ advanced organizational tools and methods.
- Follow\-Up: Can proactively identify tasks that require follow\-up, initiate necessary actions, and contribute to efficient workflow management.
- Problem\-Solving: Can proactively identify potential problems, initiate preventive measures, and propose and contribute to innovative solutions.
- Relationship Building: Can proactively seek out opportunities to expand networks, initiate collaborations, and contribute to team cohesion.
- Documentation: Can develop comprehensive documentation standards, implement best practices, and ensure documentation supports operational efficiency.
Results Orientation: Can set challenging goals for their team and lead them to achieve these goals, demonstrating a consistent track record of results.
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Skill Level Requirements
- Intermediate knowledge of Linux, Kubernetes, and orchestration.
- Knowledge of AI infrastructure, networking, and storage.
- Ability to design data center infrastructures that include hybrid cloud, hyper\-converged, software\-defined data center (SDDC), Infrastructure/Platform as a Service (IaaS/PaaS), automation, containerization, and Data Center Management Platforms – Intermediate.
- Strong knowledge of virtualization technologies, hypervisors, server virtualization, Software Defined Data Center (SDDC), containerization, and automation – Intermediate.
- Ability to effectively communicate and position complex technical products or services by understanding customer needs, articulating value propositions, and providing technical expertise throughout the sales process – Intermediate.
- Expertise in mainstream technologies including Dell Technologies, NetApp, HPE, Cisco, Pure Storage, Azure, AWS, Veeam, and Nutanix – Intermediate.
- Experience with Disaster Recovery, Business Continuity, and High Availability Solutions (backup/recovery, data protection, mirroring, active/standby, active/active, clustering) – Intermediate.
- Experience working with AI infrastructure platforms, GPU\-based computing, and modern data center technologies – Intermediate.
Understanding of AI and machine learning infrastructure concepts, including training and inference workloads – Intermediate.
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Preferred Skills
- Certifications in NVIDIA technologies (e.g., DGX Certified) or relevant OEM, cloud, or infrastructure platforms.
- Experience with NVIDIA DGX systems, HGX\-based platforms, or other AI\-optimized infrastructure solutions.
- Experience in enterprise data center technologies and solutions.
- Knowledge of data center power, cooling, and rack design considerations.
- Familiarity with AI software frameworks and platforms such as TensorFlow, PyTorch, Kubernetes, and containerized AI workloads.
- Experience supporting cloud, hybrid\-cloud, or AI infrastructure deployments.
- Knowledge of AI Factory concepts, NCPs, and AI Cloud Providers.
- Experience with Proof of Concepts (POCs), workload sizing, benchmarking, or infrastructure validation activities.
- Strong problem\-solving skills and a customer\-centric mindset.
Experience working with OEM reference architectures and solution design methodologies.
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Other Requirements
- Bachelor's degree or equivalent work experience required.
- 3–5\+ years of experience in presales engineering, solutions engineering, infrastructure consulting, or technical solution design focused on data center technologies, cloud platforms, or AI infrastructure.
- Experience designing, configuring, or supporting infrastructure solutions in enterprise data center environments.
- Familiarity with GPU technologies, AI infrastructure platforms, or high\-performance computing environments.
- Experience developing Bills of Materials (BOMs), technical configurations, sizing recommendations, and solution documentation.
- Ability to support customer discovery sessions, technical workshops, demonstrations, and proof\-of\-concept engagements.
- Strong self\-directed learning mindset with the ability to stay current in rapidly evolving AI, infrastructure, and platform ecosystems.
- Excellent communication, presentation, organizational, and time management skills.
- Ability to collaborate effectively with sales, engineering, partners, and customers in a fast\-paced technology environment.
- Willingness to work flexible schedules, including evenings and weekends, as project needs require.
- Ability to travel up to 15% for customer, partner, and industry engagements.
- Advanced certifications, published research, or open\-source contributions related to AI, infrastructure, or cloud technologies are considered a plus.
The estimated annual pay range for this position is $100,000 \- $250,000 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 $100K-$250K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $100K to $250K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
SHI International AI Hiring
SHI International has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Piscataway, NJ, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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