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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 SHI Services AI Change Enablement Consultant plays a critical role in enabling exceptional client experiences with Microsoft Copilot, Copilot agents, and adjacent AI\-powered services. This role focuses on helping clients reimagine how work gets done through design\-led thinking, intentional ways of working, and practical enablement that drives measurable outcomes.
This individual partners closely with architects, delivery teams, and client stakeholders to facilitate design\-led thinking sessions, identify moments that matter in daily work, prioritize high\-value Copilot use cases, and guide clients from ideation through adoption and value realization. In addition to experience design, the role owns core change management disciplines, including communication planning, enablement programs such as Prompt\-a\-thons, and evaluation of outcomes aligned with client objectives.
This role is not traditional training delivery. Enablement in this role means building durable capability and confidence so individuals can apply Copilot and agents across varied scenarios, not simply learning how to use specific features.
This role sits at the intersection of AI innovation, human experience, and responsible enablement. It offers the opportunity to shape how organizations build lasting capability with Copilot and workforce agents while contributing to the evolution of SHI’s Microsoft Services practice.
Role Description
- Enable clients to build new AI\-powered capabilities by leading design\-led thinking sessions and discovery workshops that uncover how work gets done, where Copilot can be applied, and why it matters.
- Help clients move beyond isolated use cases by translating design insights into repeatable Copilot and agent scenarios that can be applied across roles, workflows, and business contexts.
- Partner with architects and engineers to ensure Copilot and agent solutions are implemented in ways that reinforce scalable behaviors, decision\-making patterns, and operating models.
- Design and execute enablement strategies that empower users to apply Copilot effectively in their daily work, not just learn features or complete training.
- Develop targeted communication and enablement experiences, including Prompt\-a\-thons, scenario\-based workshops, and role\-specific learning journeys, that build confidence and fluency over time.
- Create practical enablement assets such as playbooks, prompt libraries, and internal enablement sites that support ongoing experimentation and application.
- Assess readiness, engage stakeholders, and identify barriers that limit capability development, adjusting enablement approaches based on feedback and observed usage.
- Define and measure success in terms of sustained capability, productivity, efficiency, and experience, using data and outcomes to continuously refine enablement strategies.
Behaviors and Competencies
- Communication: Can effectively communicate complex ideas and information to diverse audiences and can facilitate effective communication between others.
- Adaptability: Can proactively adapt to challenging situations, anticipate changes, and make modifications to meet the demands of changing circumstances.
- Collaboration: Can proactively seek out diverse perspectives, facilitate open communication among team members, and drive toward consensus and action.
- Presenting: Can design and deliver engaging presentations, adapting the content and style to suit the audience, context, and medium.
- Interpersonal Skills: Can communicate effectively, build relationships, resolve conflicts, and influence others in significant situations.
- Willingness to Learn: Can regularly integrate new skills and knowledge into daily work and is open to feedback and making changes accordingly.
- 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.
- Analytical Thinking: Can synthesize complex data, identify patterns, draw insights, and present findings clearly and understandably.
- Initiative: Can proactively seek out challenges, initiate projects, and contribute to innovative ideas.
Skill Level Requirements
We are looking for candidates who demonstrate strength across the following areas. Deep expertise in every tool is not required, but experience across these capabilities is important for success.
- Bachelor’s degree in a technical, business, or related field.
- Experience leading enablement, adoption, or change management efforts that help people embrace new tools and ways of working.
- Strong facilitation and communication skills, including experience leading design\-led thinking sessions, workshops, or working sessions.
- Prosci certification or formal training in change management.
- Familiarity with Microsoft Copilot, Copilot Studio, declarative or embedded agents, or similar AI\-assisted workflow patterns.
- Familiarity with using the Power Platform to design workflows, automation, or AI\-enabled experiences.
- Ability to translate business objectives into practical enablement strategies and measurable outcomes.
- Consultative mindset with the ability to work effectively across technical and non\-technical stakeholders.
- Microsoft Copilot, agent, AI, or Power Platform certifications, including:
+ Microsoft 365 Copilot and Agent Administration Fundamentals (AB 900\)
+ Microsoft Agentic AI Business Solutions Architect (AB 100\)
+ Microsoft AI Business Professional (AB 730\)
+ Microsoft AI Transformation Leader (AB 731\)
+ Microsoft Azure AI Fundamentals (AI 900\)
+ Microsoft Azure AI Engineer Associate (AI 102\)
- Exposure to governance, security, or responsible AI considerations in enterprise environments.
Candidates should not self\-select out if they do not meet all of these.
Other Requirements
- Ability to work independently and as part of a collaborative, cross\-functional team.
- Ability to travel for organizational and client engagements, up to 20%.
- Willingness to continuously learn Microsoft Copilot, AI capabilities, and enablement best practices.
- Strong ethical standards and adherence to data privacy, governance, and security policies.
The estimated annual pay range for this position is $163,0000 \- $181,000 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
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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SHI International AI Hiring
SHI International has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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