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About This Role
### Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer.
### As the only National Cancer Institute\-designated Comprehensive Cancer Center based in Florida, Moffitt employs some of the best and brightest minds from around the world. Join a dedicated team of nearly 11,000 who are shaping the future we envision. Moffitt has been recognized as a Best and Brightest Company to Work For in the Nation and is continually named one of the Tampa Bay Times’ Top Workplaces.
Summary
The Senior Director, Enterprise Automation \& AI is a strategic leader responsible for advancing Moffitt Cancer Center’s enterprise\-wide automation and operational AI initiatives. Serving as the business\-side demand and value\-realization lead for the Integrated AI Strategy, this role focuses on identifying, prioritizing, and scaling high\-impact automation opportunities across revenue cycle, scheduling, HR, finance, and other non\-clinical operations. The position partners closely with Information Technology and Enterprise Data Science to enable self\-service analytics, business intelligence, and data\-driven decision\-making across the organization, while maintaining clear accountability for business outcomes rather than platform ownership.
This leader drives enterprise prioritization, cross\-functional execution, and measurable ROI from AI, automation, and data initiatives. They serve as the primary business sponsor for data quality and enterprise reporting capabilities and lead a cross\-functional team delivering scalable solutions in complex healthcare environments. Success in this role requires deep expertise in process optimization, automation, and applied AI, combined with strong leadership and the ability to translate operational needs into technology\-enabled outcomes at enterprise scale.
The Senior Director, Data and AI Business Operations is a strategic leader responsible for advancing
Moffitt Cancer Center’s enterprise\-wide automation and operational AI initiatives. Serving as the
business\-side demand and value\-realization lead for the Integrated AI Strategy, this role focuses on
identifying, prioritizing, and scaling high\-impact automation opportunities across revenue cycle,
scheduling, HR, finance, and other non\-clinical operations. The position partners closely with Information
Technology and Enterprise Data Science to enable self\-service analytics, business intelligence, and
data\-driven decision\-making across the organization, while maintaining clear accountability for business
outcomes rather than platform ownership.
This leader drives enterprise prioritization, cross\-functional execution, and measurable ROI from AI,
automation, and data initiatives. They serve as the primary business sponsor for data quality and
enterprise reporting capabilities and lead a cross\-functional team delivering scalable solutions in complex
healthcare environments. Success in this role requires deep expertise in process optimization,
automation, and applied AI, combined with strong leadership and the ability to translate operational needs into technology\-enabled outcomes at enterprise scale.
Minimum Skills/Specialized Training Required
Strong expertise in business process analysis and optimization, with the ability to identify automation
opportunities and design scalable, future\-state workflows
Solid understanding of automation and AI concepts in operational settings (e.g., RPA, intelligent
automation, predictive analytics, and AI\-enabled workflows)
Experience with business intelligence and self\-service analytics, including the ability to translate
business needs into dashboards and insights that drive decision\-making
Familiarity with modern data and automation platforms and tools (e.g., RPA solutions, BI tools, cloud
ecosystems), with the ability to partner effectively with IT and data teams
Demonstrated ability to build and communicate business cases, quantify ROI, and link automation and
AI initiatives to financial and operational outcomes
Strong program and project leadership skills, including managing multiple initiatives and driving
execution in complex environments
Understanding of healthcare operations, particularly areas such as revenue cycle, scheduling, or
workforce management, strongly preferred
Experience with change management and adoption, including stakeholder engagement, training, and
driving sustained use of automation solutions
Ability to translate operational challenges into technology\-enabled solutions, working across business,
technical, and data teams
Strong analytical thinking and problem\-solving skills, with the ability to turn data into actionable insights
Strong executive presence, communication, and stakeholder management skills, with the ability to
influence senior leadership and cross\-functional partners
Minimum Experience Required
Minimum 8 years of progressive experience in enterprise automation, process engineering, AI
deployment, data management, or operational analytics within healthcare or similarly complex environments.
3 years in a leadership role with responsibility for enterprise automation, RPA, AI implementation, or operational transformation initiatives
Demonstrated track record of delivering measurable operational improvements and ROI through automation and AI (e.g., revenue cycle, scheduling, financial processes, or workforce optimization), including experience quantifying and reporting outcomes to executive leadership
Experience leading cross\-functional teams and driving adoption of automation and analytics solutions in environments requiring significant change
management
Experience working with automation and analytics technologies, including RPA platforms (e.g., UiPath, Automation Anywhere, Power Automate), BI/analytics tools (e.g., Power BI, Tableau, Sigma), and process analysis or mining tools
Familiarity with AI\-enabled automation approaches, including intelligent document processing (IDP) and emerging AI/LLM\-driven workflow orchestration
Experience operating within complex, regulated environments, with an understanding of healthcare operations (e.g., revenue cycle, scheduling, workforce planning) strongly preferred
Proven ability to deliver results in a federated operating model, partnering effectively with IT, EHR,
ERP/HCM, and data platform teams to drive business outcomes
Minimum Education
Bachelor's Degree Computer Science, Information
Systems, Industrial Engineering,
Data Science, Business
Analytics, Health Systems
Engineering, or a related
discipline
Preferred Education
Master's Degree Data Science, Health Informatics,
Engineering, or Business
Administration with a technology
or analytics focus
LICENSURE/CERTIFICATION
Recognized certifications in project delivery and
process improvement (e.g., Project Management
Professional \[PMP], Lean Six Sigma Black Belt or
Green Belt)
Relevant automation or RPA platform certifications
(e.g., UiPath, Automation Anywhere, Microsoft
Power Automate)
Certifications in business intelligence, analytics, or
data platforms (e.g., Microsoft Power BI, Tableau,
Databricks, or equivalent)
Salary Range
$192,178\.48 \- $289,057\.60*Salary ranges posted for this position represent the expected base pay range for the role. Actual compensation may vary based on location and a variety of job\-related factors, including experience, skills, education, and internal equity among Team Members in similar positions.*
*We are committed to maintaining fair and equitable pay practices and regularly review compensation to ensure alignment across our workforce.*
Moffitt Career Site
*If you have the vision, passion, and dedication to contribute to our mission,*
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*then we have a place for you!*
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1\. Equal Employment Opportunity
Moffitt Cancer Center is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, or protected veteran or disabled status. We seek candidates whose skills, and personal and professional experience, have prepared them to contribute to our commitment to diversity and excellence.
2\. Reasonable Accommodation
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment. Moffitt endeavors to make moffitt.org/careers accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact one of the Human Resources receptionists by phone at 813\-745\-7899 or by email at [email protected]. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Read more information about your EEO rights under the law.
Transparency in Coverage Rule
Salary Context
This $192K-$289K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Moffitt Cancer Center, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($240K) sits 10% above the category median. Disclosed range: $192K to $289K.
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.
Moffitt Cancer Center AI Hiring
Moffitt Cancer Center has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Tampa, FL, US, Land O' Lakes, FL, US. Compensation range: $161K - $331K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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