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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
The Senior Director, Data & AI leads SHI's AI + Infrastructure organization within the Microsoft Services Practice. This leader brings together Agentic Solutions, Cloud Platform, Data & Intelligence, and AI Security & Governance to build and deliver connected Microsoft solutions across Azure, Microsoft Fabric, Microsoft Foundry, agents, data platforms, and the security and governance capabilities required to operate them responsibly.
The Senior Director runs the organization as a business, with ownership of service maturity, solution development, headcount planning, utilization, delivery quality, capacity, pipeline support, and measurable client outcomes. This role leads managers and practice leaders responsible for Agentic Solutions, Cloud Platform, Data & Intelligence, and AI Security & Governance and remains accountable for their effectiveness, development, and delivery performance.
Aligned to Microsoft's Fabric IQ and Foundry IQ investments, this role connects cloud foundation, business data, enterprise knowledge, agentic experiences, and trust. The Senior Director helps SHI move clients from isolated pilots to secure, governed, production-ready platforms and solutions while creating repeatable paths for growth.
This role sits at the center of SHI's Microsoft Services growth plan, turning Fabric IQ and Foundry IQ alignment into a working operating model that builds stronger solutions, improves delivery execution, develops talent, and creates repeatable paths for growth.
This is a remote position with a Home Office setup based in New York City, Charlotte, Chicago, Atlanta, Austin, Denver, Los Angeles, San Francisco, or Seattle. Role Description
Strategic Leadership and Practice Development
- Set and execute a unified direction for AI + Infrastructure, connecting Agentic Solutions, Cloud Platform, Data & Intelligence, and AI Security & Governance through one operating model.
- Translate Microsoft's Fabric IQ, Foundry IQ, Azure, and agent investments into a clear SHI services roadmap, capability plan, and investment agenda.
- Establish clear ownership, decision rights, handoffs, delivery standards, and quality expectations across the organization.
- Build modular, repeatable offerings that combine cloud, data, AI, and governance capabilities around defined customer outcomes.
- Use portfolio, pipeline, delivery, and capacity data to prioritize investments, manage tradeoffs, and improve practice health.
People Leadership Through Managers and Directors
- Lead, coach, and develop the leaders responsible for Agentic Solutions, Cloud Platform, Data & Intelligence, and AI Security & Governance.
- Set measurable expectations for customer experience, delivery quality, utilization, capability development, financial performance, and team health.
- Build leadership depth through regular one-on-ones, development planning, succession planning, and clear career paths.
- Own workforce planning across the organization, including hiring, onboarding, retention, skills development, and capacity alignment to backlog and pipeline.
- Create a culture of shared accountability in which teams solve for the full customer outcome rather than optimize isolated technology workstreams.
Technical Direction Across Data, AI, and Security
- Maintain a platform-aware perspective across Agent 365, Copilot Studio, Microsoft Foundry, Model Context Protocol, Azure, landing zones, platform engineering, FinOps, Microsoft Fabric, OneLake, data engineering, data visualization, and data readiness.
- Embed Entra Agent ID, Microsoft Purview, Defender for Cloud and AI, Security Copilot, Microsoft Sentinel, and Defender XDR into AI and platform solutions where those capabilities strengthen identity, data protection, observability, governance, and threat protection.
- Sponsor reusable reference architectures, delivery frameworks, statements of work, work breakdown structures, templates, and accelerators.
- Rely on practice leaders and principal architects for deep technical direction while maintaining alignment across disciplines.
- Create a practical path from assessment and platform readiness through implementation, adoption, optimization, and transition to managed services.
Delivery Excellence and Client Outcomes
- Provide executive oversight for complex, multi-workstream engagements that span cloud, data, AI, agents, security, and governance.
- Hold leaders accountable for scope quality, staffing readiness, delivery methodology, risk management, timelines, budgets, and customer communication.
- Review project health, utilization, realization, margin, capacity, and quality signals and act early when performance moves off plan.
- Protect customer trust by ensuring teams make supportable commitments, apply appropriate governance, and transition solutions into sustainable operations.
- Serve as an executive sponsor and trusted advisor for strategic clients, connecting platform decisions to measurable business outcomes.
Business Growth and Market Development
- Grow AI + Infrastructure services revenue by improving solution relevance, supporting strategic pursuits, expanding enterprise client relationships, and developing repeatable offers.
- Partner with Solution Architecture, Sales, Project Management, Digital Workplace, and Managed Services to shape opportunities and deliver connected customer outcomes.
- Manage utilization, capacity, backlog, pipeline health, and delivery performance to improve profitability and support sustainable growth.
- Build strong relationships with Microsoft field, engineering, and partner teams to align priorities, improve co-sell execution, and strengthen SHI's market credibility.
- Capture customer evidence, references, and measurable outcomes that support retention, expansion, and market recognition.
Executive and Cross-Functional Communication
- Serve as an executive advisor to enterprise clients, connecting business priorities to Microsoft cloud, data, AI, agent, security, and governance strategies.
- Communicate practice priorities, portfolio health, performance, risks, and investment needs to senior leadership, including the Vice President, Microsoft Services.
- Run a consistent operating cadence across leaders that covers pipeline, staffing, project health, financial performance, capability development, and customer outcomes.
- Turn operating data into decisions and actions rather than reporting activity without accountability.
- Escalate delivery, capacity, security, compliance, and customer risks early, with clear options and recommended actions.
Behaviors and Competencies
- Strategic Thinking: Can analyze complex situations, drive organizational transformation, and adapt strategies to changing market conditions.
- Leadership: Can lead strategic team initiatives, inspire others to take leadership roles, and foster a culture of shared responsibility and continuous improvement.
- Communication: Can lead and model exceptional communication at all levels of the organization, develop and implement communication strategies, and coach others to improve their communication skills.
- Collaboration: Can lead complex team projects, inspire others to collaborate effectively, and foster a culture of mutual respect and shared purpose.
- Problem-Solving: Can lead strategic problem-solving initiatives, inspire others to improve their problem-solving skills, and foster a culture of proactive problem-solving.
- Customer-Centric Mindset: Can lead strategic initiatives focused on improving the overall customer experience. Inspires and mentors others to adopt a customer-centric approach, fostering a culture of customer focus throughout the organization.
- Results Orientation: Can inspire a culture of results-orientation across the organization, setting high standards and holding everyone accountable for achieving results.
- Adaptability: Can drive strategic transformations, inspire others to embrace change, and foster a culture of continuous adaptation.
- Strategic Implementation: Can lead the development and execution of comprehensive strategic plans, inspire and guide teams, and drive organizational change to achieve strategic objectives.
- Continuous Improvement: Can lead others in identifying and implementing major improvements and proactively drive continuous improvement across the organization.
Skill Level Requirements
- Bachelor's degree in a technical, business, or related field, or equivalent practical experience.
- 10 or more years of progressive leadership experience in technology services, consulting, cloud, data, AI, or related Microsoft practices.
- 5 or more years leading managers, directors, senior technical leaders, or multi-discipline delivery organizations with accountability for people, delivery, and business performance.
- Demonstrated success building or scaling professional services capabilities, repeatable offerings, and complex enterprise delivery motions.
- Strong working knowledge of Microsoft Azure, Fabric, Foundry, Copilot Studio, agents, data platforms, and the identity, security, and governance services that support enterprise adoption.
- Experience managing utilization, capacity, staffing, delivery quality, portfolio health, and financial performance.
- Ability to engage enterprise executives, translate business priorities into technology and services roadmaps, and guide complex decisions.
- Track record of partnering across sales, presales, project management, managed services, and technology partners to grow customer relationships.
- Relevant certifications may include Microsoft Fabric, Azure AI, Azure architecture, cybersecurity architecture, identity, or agentic AI credentials. Equivalent experience and demonstrated leadership may substitute for specific certifications.
- Candidates should not self-select out if they do not meet all of these qualifications.
Your Success at SHI
People who are successful in this role tend to demonstrate:
- Strong ownership of a multi-discipline services organization, connecting technical direction with people leadership and business performance.
- The ability to connect cloud, data, agents, and governance into practical solutions that clients can adopt and operate.
- A talent development mindset that grows managers and practice leaders into confident, accountable leaders.
- Comfort using portfolio and delivery data to guide staffing, coaching, investment, and escalation decisions.
- Strong collaboration across Solution Architecture, Delivery, Project Management, Digital Workplace, Managed Services, Sales, and Microsoft.
- A genuine commitment to customer experience, delivery quality, security, compliance, and the long-term growth of SHI's Microsoft Services practice.
Other Requirements
- Ability to work independently while collaborating effectively with executives, managers, technical leaders, sales teams, and customer stakeholders.
- Ability to travel to support client engagements, Microsoft alignment, leadership meetings, and internal enablement, up to 25%.
- Commitment to continuous learning as Microsoft cloud, data, AI, agent, and security platforms evolve.
- Strong ethical standards with disciplined attention to privacy, governance, compliance, security, and responsible AI.
The estimated annual pay range for this position is $225,000 - $325,000 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 $225K-$325K range is above the 75th percentile 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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($275K) sits 30% above the category median. Disclosed range: $225K to $325K.
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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