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Job Purpose
The IT Program Manager \- Digital \& AI is responsible for leading complex, cross\-functional technology programs that enable enterprise growth, digital transformation, and AI\-driven innovation. Operating within the enterprise IT portfolio, this role oversees large\-scale initiatives and emerging AI and data programs that support strategic business objectives.
This position provides end\-to\-end program leadership across multiple concurrent initiatives, ensuring alignment to enterprise priorities, disciplined governance, proactive risk mitigation, and measurable value realization. In addition to managing direct reports, the role partners closely with executive stakeholders, including the Chief Digital \& AI Officer and other AI Leaders, and owns executive\-level reporting, governance, and steering communications. The Senior IT Program Manager drives structured execution while strengthening program management capabilities across the organization.
How You Make an Impact (Job Accountabilities)
Enterprise Program Leadership (30%)
- Lead complex, multi\-project programs aligned to digital, AI, and enterprise technology roadmaps.
- Oversee large\-scale initiatives and AI/data programs supporting enterprise growth strategies.
- Ensure integration across workstreams, proactively managing scope, dependencies, risk, and timeline.
- Drive value realization by aligning program outcomes to defined business objectives and measurable performance indicators.
Executive Governance \& Strategic Alignment (20%)
- Own executive\-level program reporting, dashboards, and steering committee communications.
- Partner with the Chief Digital \& AI Officer, IT leadership, and cross\-functional executives to align priorities and sequencing.
- Provide clear visibility into program health, delivery risks, and organizational readiness.
- Influence enterprise decision\-making through structured analysis, scenario planning, and transparent communication.
People Leadership \& Capability Development (15%)
- Directly manage and develop a small team of Project Managers, providing coaching, performance management, and workload prioritization.
- Establish consistent delivery standards, governance rigor, and accountability across assigned programs.
- Build bench strength within the PMO to support expanding AI and enterprise technology initiatives.
Portfolio Alignment \& Delivery Oversight (15%)
- Ensure programs are sequenced and resourced in alignment with enterprise priorities.
- Identify cross\-program risks, interdependencies, and optimization opportunities.
- Support planning and forecasting processes in partnership with IT leadership.
Cross\-Functional \& Matrix Leadership (15%)
- Lead cross\-functional teams spanning IT, Digital, AI, Data, Analytics, and Business functions.
- Coordinate matrix resources to support enterprise AI and digital growth initiatives.
- Remove organizational barriers and resolve escalated issues to maintain program momentum.
Continuous Improvement \& Governance Maturity (5%)
- Strengthen program management frameworks, reporting standards, and governance models within the PMO.
- Promote scalable delivery practices to support increasing program complexity and organizational growth.
What You Bring to the Role (Job Qualifications / Education / Skills / Requirements / Capabilities)
Education
Bachelor’s degree in Information Technology, Computer Science, Engineering, Business, or related field required.
Master’s degree preferred.
Experience
- 12\+ years of progressive experience in IT project and program management.
- 5\+ years leading complex, enterprise\-level programs with multiple concurrent initiatives.
- Demonstrated experience supporting digital transformation, AI, data, ERP, or enterprise technology initiatives.
- Experience working with executive stakeholders and preparing executive\-level communications and reporting.
- Proven experience leading and developing project managers or delivery leads.
Certifications
- PMP certification required
- Agile, SAFe, or related certifications preferred.
Technical \& Leadership Capabilities
- Strong program governance and delivery oversight expertise.
- Ability to manage integrated program roadmaps with multiple concurrent workstreams.
- Executive\-level communication and presentation skills.
- Experience building structured reporting dashboards and performance metrics.
- Strong risk identification and mitigation planning capability.
- Ability to influence cross\-functional leaders without direct authority.
- Demonstrated ability to balance strategic priorities with disciplined execution.
Competencies Desired
- Enterprise mindset with strong business acumen
- Strategic thinking with disciplined execution
- Executive presence and sound judgment
- Accountability and ownership of outcomes
- Collaborative leadership across matrixed teams
- Ability to navigate ambiguity and scale evolving initiatives
Working Conditions / Physical Requirements / Travel Requirements
Regional remote role with proximity to Lexington, KY headquarters. Hybrid engagement required for key meetings and strategic planning sessions. Primarily office\-based work with extended periods of computer use.
Travel Requirements:
Approximately 15% travel, primarily domestic, to support executive alignment, program milestones, and cross\-functional collaboration.
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 Valvoline Global Operations, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Valvoline Global Operations AI Hiring
Valvoline Global Operations has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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