Interested in this AI/ML Engineer role at Enterprise Mobility?
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Overview
Enterprise Mobility is a leading provider of mobility solutions, owning and operating the Enterprise Rent-A-Car, National Car Rental and Alamo Rent A Car brands through its integrated global network of independent regional subsidiaries. Enterprise Mobility and its affiliates offer extensive car rental, carsharing, truck rental, fleet management, retail car sales, as well as travel management and other transportation services, to make travel easier and more convenient for customers.
Privately held by the Taylor family of St. Louis, Enterprise Mobility together with its affiliate Enterprise Fleet Management manages a diverse fleet of 2.4 million vehicles and accounted for nearly $39 billion in revenue through a network of more than 9,500 fully-staffed neighborhood and airport rental locations in more than 90 countries and territories.
At the center of it all, our dedicated IT teams innovate, design and develop the technology that is redefining how customers rent, buy and share vehicles from our family of brands. Here, you will be part of a diverse and talented team that creates and delivers powerful technology solutions for our customers and employees across the world with the resources and support to develop in a variety of career paths.
As an Enterprise Mobility team member, we offer an excellent package with market-competitive pay, comprehensive healthcare packages, 401k matching & profit sharing, schedule flexibility, work from home opportunities, paid time off, and organizational growth potential.
This position is eligible for a hybrid work schedule, which includes the ability to work from home, while also requiring to be in person as needed at our Lakeside and/or Clayton Corporate HQ campus locations in St. Louis, Missouri.
Responsibilities
As a member of the Workforce Enablement Product leadership team, the IT Director – AI Enablement is responsible for shaping how Enterprise Mobility evaluates, enables, and applies AI capabilities across the organization. This role connects business priorities, technology capabilities, governance practices, and organizational readiness to help the company effectively leverage AI to solve business challenges and unlock new opportunities.
Working across business, technology, and corporate functions, this leader will establish the frameworks, standards, and partnerships needed to guide how AI is evaluated, prioritized, and applied across the enterprise. This is a unique opportunity to help shape the future of work at Enterprise Mobility byestablishingthe foundations that enable AI to become a trusted, scalable, and sustainable capability across the enterprise. In This Role You Will Be Responsible For: AI Strategy & Operating Model Define and evolve Enterprise Mobility's AI strategy, operating model, and investment priorities. Establish decision-making frameworks that guide how AI opportunities are evaluated, prioritized, and scaled across the enterprise. AI Center of Excellence Leadership Establish and mature the AI Center of Excellence as the enterprise resource for AI standards, best practices, governance coordination, and organizational enablement. Create scalable frameworks that help business teams evaluate, prioritize, and apply AI capabilities in support of business objectives. AI Adoption & Organizational Enablement Drive enterprise AI literacy, workforce readiness, and adoption efforts. Help business teams identify high-value use cases and successfully incorporate AI into business processes while fostering a culture of learning, experimentation, and innovation. Responsible AI & Governance Partnership Partner with Security, Legal, Compliance, Risk, Data & Analytics, and Architecture leaders to establish policies and guardrails that support responsible, secure, and compliant use of AI. Enterprise Influence & Strategic Partnerships Build strong partnerships across business functions, Product organizations, Architecture, Engineering, Data & Analytics, and external technology partners. Lead through influence to align priorities, shape investments, and help business teams successfully incorporate AI capabilities into their strategies and operations. Leadership & Capability Development Develop and inspire future leaders, AI champions, and practitioners across the organization. Foster a culture of innovation, continuous learning, and knowledge sharing while helping build the capabilities needed to support long-term AI success. What We're Looking For: The ideal candidate combines strategic leadership, business acumen, and a strong understanding of emerging technologies with the ability to influence enterprise direction and drive organizational change. This role is well suited for leaders with experience in technology strategy, digital transformation, product management, business innovation, data and analytics, enterprise architecture, consulting, or related disciplines who are energized by the opportunity to help shape how AI transforms the future of work.* Bring a strong understanding of AI capabilities, opportunities, risks, and emerging industry trends.
- Demonstrate the ability to define strategy, establish operating models, and align technology investments with business priorities.
- Excel at building trusted relationships with executives, business leaders, and technology teams.
- Lead through influence and successfully drive complex cross-functional initiatives.
- Balance innovation, governance, risk management, and organizational readiness when establishing priorities.
- Have a passion for organizational change, workforce enablement, and helping people adopt new ways of working.
- Be comfortable operating in ambiguity and helping build new capabilities, standards, and processes.
- Inspire curiosity, continuous learning, and responsible experimentation with emerging technologies.
Successful leaders in this position will:* Establish a trusted enterprise approach for evaluating and applying AI capabilities.
- Increase organizational readiness and confidence in the use of AI.
- Enable business teams to successfully incorporate AI into their strategies and operations.
- Evolve AI from a centrally guided initiative into a sustainable federated model of ownership and innovation.
- Strengthen Enterprise Mobility's ability to adapt, innovate, and compete through the responsible use of AI.
- Develop a network of AI champions and future leaders who accelerate learning and innovation across the organization.
Equal Opportunity Employer/Disability/Veterans
Qualifications
Required:* Must be presently authorized to work in the U.S. without a requirement for work authorization sponsorship by our company for this position now or in the future
- Must reside in St. Louis, Missouri, or immediate surrounding area
- 10+ years of relevant leadership experience in AI, data, automation, digital transformation, or related disciplines.
- 5+ years of experience leading large-scale technology transformation initiatives.
- Experience leading the design, deployment, and scaling of enterprise AI solutions and large technical integrations.
- Demonstrated success developing technology strategies and translating AI capabilities into measurable business outcomes.
- Deep understanding of AI's impact on work, leadership, adoption, trust, governance, and ethical decision-making.
- Strong business and financial acumen with experience managing investments, budgets, resource planning, and value realization.
- Proven ability to influence executive stakeholders and lead cross-functional initiatives.
- Commitment to incorporating security, privacy, compliance, and responsible AI principles into all technology decisions and solutions.
Preferred:* Master's degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, Engineering, Business Administration, or a related field.
- Experience establishing AI governance programs, Centers of Excellence, or enterprise AI transformation initiatives.
- Experience with modern AI platforms, cloud ecosystems, generative AI, agentic AI, and enterprise data platforms, specifically the Microsoft Copilot suite of products.
- Experience leading AI adoption, organizational change management, and workforce enablement programs.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Enterprise Mobility, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Enterprise Mobility AI Hiring
Enterprise Mobility has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in St. Louis, MO, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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