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About This Role
Job Purpose:
The Vice President, Enterprise Data AI is the executive accountable for driving the enterprise-wide execution, adoption, governance, and value realization of TD SYNNEXs Data AI strategy. Reporting to the SVP, Digital Innovation Enterprise Architecture, this leader converts enterprise strategy into delivered outcomes across the Data Platform, AI Factory, Data Governance, Business Intelligence, and AI Enablement functions, and owns the business results those functions produce.
The role carries accountability for the value of the Data AI portfolio, not only its delivery. It sets the operating model, prioritizes investment, and establishes the governance and responsible AI controls under which AI is deployed globally. The VP partners with business, technology, security, finance, and legal leaders across four regions to align priorities, remove barriers, and accelerate outcomes that improve decision quality, productivity, and financial performance.
Responsibilities:
- Own execution of the enterprise Data AI roadmap, ensuring strategic priorities are delivered consistently across regions, functions, and business units. Approve sequencing and trade-off decisions across speed, quality, risk, and cost, and hold director-level leaders accountable for committed outcomes.
- Own the benefits case for the Data AI portfolio from investment approval through post-implementation review. Partner with business leaders and Finance to set baselines, agree measurable value targets, embed capabilities into everyday business processes, and report captured value against commitment. Redirect or terminate initiatives that are not realizing committed value.
- Direct the development and operation of the enterprise Data Platform, AI Factory, and related AI capabilities. Set platform roadmap and approve platform investment so the estate remains scalable, secure, reliable, and positioned to support future business needs.
- Own enterprise data governance and responsible AI practice, including the AI use-case approval process. Accountable for data quality and partner with other teams on security, privacy, regulatory compliance, and model risk management being embedded in the design, deployment, and ongoing operation of Data AI solutions.
- Set priorities, performance measures, governance forums, and execution processes so resources concentrate on the highest-impact initiatives. Monitor progress, escalate and resolve enterprise risks, and provide transparency into delivery commitments and outcomes.
- Own the operating and investment budget for Data AI, approve expenditure within delegated authority, and lead strategic vendor selection, negotiation, and performance management. Drive unit-cost improvement while sustaining investment in capabilities that create long-term value.
- Build and lead a global organization of directors, managers, and technical professionals. Define the operating model and organizational design, hire and develop director-level leaders, set succession plans for critical roles, and cultivate a culture of accountability, collaboration, and continuous improvement.
Knowledge, Skills and Experience:
- 15 Years of relevant work experience in in data, AI/ML, engineering, and/or platform leadership roles, with executive-level accountability for delivery outcomes.
- 8 Years of experience directly managing / supervising employees
- Demonstrated accountability for an operating budget of $5M or greater and an investment portfolio of comparable scale.
- Hands-on familiarity with the Microsoft ecosystem (e.g., Azure Foundry, Microsoft Copilot, Copilot Studio, modern data platforms (e.g., Snowflake, Databricks) and foundational AI models (openAI, Anthropic, etc).
- Experience with FinOps best practices for cloud and AI cost management, charge back and show back models.
- Proven record of delivering measurable business value from data and AI investments, evidenced by quantified outcomes.
- Deep working knowledge of modern data platform architecture, AI and generative AI deployment at scale, and data governance frameworks.
- Experience establishing responsible AI and data governance practice in a regulated, multi-jurisdiction environment.
- Ability to bridge communication gaps and drive cross-functional alignment to deliver value without conflicting implementations.
- Executive presence and demonstrated effectiveness influencing C-suite peers and reporting to board-level audiences.
- Experience managing globally distributed teams across NA, EMEA, APJ and LATAM preferred.
- Experience leading data and AI transformation through significant business change, such as an integration, divestiture, or restructuring preferred.
- Experience in distribution, supply chain, or another high-volume, low-margin operating model preferred.
- Bachelors Degree or equivalent work experience in a related field required.
- Masters / Post Graduate Degree required.
- Able to execute instructions and to request clarification when needed.
- Able to demonstrate complex problem solving, critical thinking, and decision-making.
- Able to recognize and attend to important details with accuracy and efficiency.
- Able to communicate clearly and convey necessary information.
- Able to converse and write effectively in English and other local / regional language.
- Able to create and conduct formal presentations.
- Able to interact effectively with high levels of management (managers above.)
- Able to negotiate skillfully and promote/sell ideas persuasively.
- Understand, communicate, and collaborate effectively with people across various identities.
- Possesses strong leadership skills with a willingness to lead, create new ideas, and be assertive.
- Possesses strong organizational and time management skills, driving tasks to completion.
- Able to adjust readily to change and adapt as needed.
- Able to constructively work under stress and pressure when faced with high workloads and deadlines.
- Able to maintain and promote social, ethical, and organizational standards in conducting internal and external business activities.
- Able to work independently with minimum supervision.
- Able to maintain confidentiality of sensitive information.
- Able to effectively multi-task.
- Able to collaborate and build solid, effective working relationships with others.
- Able to exhibit ability to be sensitive to the needs, concerns, and feelings of others.
- Able to use relevant computer systems and applications at an advanced level.
- Able to quickly learn/adapt to new systems and technology.
- Ability to stay aware and alert while performing work.
Working Conditions:
- Global travel required.
- Remote / Work-from-home.
- Local (in-country) travel required.
- Professional, office environment.
- Frequent Travel Required (25%).
#LI-Remote
At TD SYNNEX, our values guide everything we do: Together, We Own It, We Dare to Go, We Grow and Win, and above all, We Do the Right Thing. These principles shape how we work with each other, our partners, and our communities as we drive innovation and create lasting impact.
What’s In It For You?
- Elective Benefits: Our programs are tailored to your country to best accommodate your lifestyle.
- Grow Your Career: Accelerate your path to success (and keep up with the future) with formal programs on leadership and professional development, and many more on-demand courses.
- Elevate Your Personal Well-Being: Boost your financial, physical, and mental well-being through seminars, events, and our global Life Empowerment Assistance Program.
- Diversity, Equity Inclusion: It’s not just a phrase to us; valuing every voice is how we succeed. Join us in celebrating our global diversity through inclusive education, meaningful peer-to-peer conversations, and equitable growth and development opportunities.
- Make the Most of our Global Organization: Network with other new co-workers within your first 30 days through our onboarding program.
- Connect with Your Community: Participate in internal, peer-led inclusive communities and activities, including business resource groups, local volunteering events, and more environmental and social initiatives.
Don’t meet every single requirement? Apply anyway.
At TD SYNNEX, we’re proud to be recognized as a great place to work and a leader in the promotion and practice of diversity, equity and inclusion. If you’re excited about working for our company and believe you’re a good fit for this role, we encourage you to apply. You may be exactly the person we’re looking for!
We are an equal opportunity employer and committed to building a team that represents and empowers a variety of backgrounds, perspectives, and skills. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, gender identity or expression, sexual orientation, protected veteran status, disability, genetics, age, or any other characteristic protected by law.
TD SYNNEX is an E-Verify company
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TD SYNNEX, 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 $212,040 based on 13,780 positions with disclosed compensation.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
TD SYNNEX AI Hiring
TD SYNNEX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Clearwater, FL, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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