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About This Role
Primary City/State:
Phoenix, Arizona
Department Name:
IT Info Tech Admin-Corp
Work Shift:
Day
Job Category:
Information Technology
The SVP, Chief AI, Data & Infrastructure Officer is the organization’s intelligence trailblazer—an architect of transformational change who unites cutting‑edge AI, enterprise‑grade data ecosystems, and modern infrastructure into a powerful engine for innovation. This leader turns possibility into performance, accelerating growth, enabling new business models, and propelling the company into a future where intelligence fuels every decision, interaction, and outcome.
Your pay and benefits are important components of your journey at Banner Health. This opportunity includes the option to participate in a variety of health, financial, and security benefits. In addition, this position may be eligible for our Management Incentive Program as part of your Total Rewards package.Within Banner Health Corporate, you will have the opportunity to apply your unique experience and expertise in support of a nationally-recognized healthcare leader. We offer stimulating and rewarding careers in a wide array of disciplines. Whether your background is in Human Resources, Finance, Information Technology, Legal, Managed Care Programs or Public Relations, you'll find many options for contributing to our award-winning patient care.
POSITION SUMMARY
This position serves as a strategic enterprise leader responsible for advancing the organization’s artificial intelligence, data, infrastructure, and operational technology strategies. This senior leader role ensures that foundational technology and infrastructure, data, and AI capabilities enable clinical excellence, operational performance, and digital innovation aligned with Banner Health’s mission and growth strategy. As a senior member of the Technology Leadership Team, the CADIO partners closely with digital, engineering, and business leaders to build a secure, scalable, and intelligent technology ecosystem. This role drives the enterprise AI strategy, leading the development, implementation, and governance of AI capabilities that improve care delivery, decision-making, and workforce efficiency.
CORE FUNCTIONS
- AI Strategy and Implementation -Work closely with CTO to define and lead Banner Health’s enterprise AI strategy, ensuring alignment with clinical, operational, and digital transformation priorities. Partner with business and clinical executives to identify, prioritize, and scale high-value AI use cases that enhance care delivery, efficiency, and outcomes. Establish and mature AI platforms, frameworks, and governance models that support responsible, secure, and scalable AI deployment across the enterprise. Drive integration of AI and automation into clinical, operational, and administrative workflows to improve quality, reduce cost, and enhance workforce productivity. Champion ethical and transparent AI practices, ensuring compliance, explainability, and bias mitigation. Cultivate AI literacy and adoption across leadership and operational teams through education and change management initiatives.
- Data and Analytics - Define and lead the enterprise data and analytics vision and strategy, positioning data as a strategic asset that powers clinical excellence, operational agility, and innovation. Advance the evolution of the enterprise cloud data ecosystem, integrating diverse data sources, including clinical, operational, payer, and external data, into a unified, intelligent data platform that supports large-scale analytics and AI. Architect and modernize data platforms and pipelines to deliver real-time, high-quality, and interoperable data, leveraging modern paradigms such as data mesh, streaming, and serverless architectures for scalability and speed. Establish and own enterprise data governance and stewardship frameworks, ensuring data integrity, lineage, privacy, and compliance while enabling secure, democratized access through self-service analytics and governed data marketplaces. Drive advanced analytics capabilities, embedding predictive, prescriptive, and real-time insights into clinical, operational, and business workflows to enable data-driven decision-making and performance optimization. Lead the enterprise analytics portfolio, unifying clinical, operational, financial, and experience metrics into shared KPIs and dashboards that provide real-time visibility to executives and frontline leaders. Foster a data-driven culture by partnering with digital, clinical, and operational leaders to translate insights into measurable impact, optimizing care pathways, health outcomes, and resource utilization and workflows.
- Cloud, Infrastructure and Operations - Lead enterprise-wide cloud transformation and infrastructure modernization initiatives, including multi-cloud architecture, hybrid platform optimization, and data center exit strategies, to deliver resilient, scalable, and cost-efficient environments. Drive adoption of cloud-native technologies, infrastructure-as-code (IaC), and platform engineering practices to accelerate deployment, standardize environments, and enable self-service capabilities across teams. Advance AI and automation-driven operations (AIOps) and observability frameworks to enhance service reliability, predictive monitoring, and proactive issue resolution across cloud and on-premise systems. Partner with cybersecurity and network engineering teams to ensure secure, compliant, and high-performing multi-cloud and network environments, with embedded governance and cost optimization controls. Lead the enterprise IT Service Desk and end-user experience functions, transforming support through automation, analytics, and digital workflows to improve responsiveness, satisfaction, and operational efficiency. Manage physical technology infrastructure across hospitals and facilities, including network and cabling systems, hardware lifecycle management, and on-site connectivity standards. Oversee enterprise IT service delivery, system performance, and software asset management, driving operational excellence through automation, continuous improvement, and real-time insights. Champion a cloud-first culture that emphasizes agility, scalability, and innovation, enabling rapid response to evolving business, data, and AI workloads.
- Software Engineering - Lead enterprise software engineering and application modernization initiatives, ensuring delivery of secure, scalable, and interoperable solutions that enable business agility. Advance cloud-native,
DevSecOps, and Agile engineering practices to accelerate delivery, improve quality, and support continuous innovation. Establish enterprise architecture governance and software standards, ensuring compliance with healthcare regulations (HIPAA, HITRUST) and data exchange protocols (FHIR, HL7). Partner with Digital Product, Data, and Infrastructure teams to ensure solutions are interoperable, secure, and optimized for performance across hybrid cloud environments (e.g., AWS, Azure). Oversee multi-channel application development (consumer web, mobile, and APIs) ensuring interoperability with enterprise systems such as EMR, data platforms, and business applications. Drive architectural governance and platform reusability to reduce complexity, optimize performance, and ensure long-term maintainability.
- Technology Management and Biomedical Devices - Provide executive leadership for technology management and biomedical engineering functions, overseeing the lifecycle of clinical and connected devices. Lead device refresh, replacement, and interoperability programs to ensure reliability, safety, and seamless data integration across care environments. Partner with clinical and operational leaders to pilot and scale emerging technologies, including robotics, automation, and intelligent devices, that enhance efficiency and patient outcomes. Align technology investments with Banner Health’s mission to deliver safe, innovative, and cost-effective care supported by next-generation device technologies.
- Technology Leadership and Collaboration - Partner closely with the Technology Leadership Team to ensure alignment, shared accountability, and cohesive execution across AI, data, digital, engineering, and infrastructure domains. Collaborate with clinical, operational, and administrative executives to ensure technology strategies directly advance systemwide goals for growth, quality, access, and experience. Serve as a visible technology and innovation ambassador across the organization and industry, representing Banner Health in executive, governance, and external forums. Champion cross-functional collaboration and enterprise alignment, ensuring that technology, data, and infrastructure initiatives deliver measurable business and clinical impact. Enable operations, clinical and research teams centralized infrastructure and distributed product ownership.
7.. Leadership and Talent Development - Build, mentor, and empower a high-performing, multidisciplinary leadership team spanning AI, data, infrastructure, engineering, and technology management disciplines. Foster a culture of innovation, inclusion, accountability, and excellence, empowering teams to challenge convention and deliver meaningful outcomes. Champion professional growth and succession development, cultivating future technology and data leaders through mentoring, career development, and leadership readiness initiatives. Define clear performance metrics and capability frameworks to ensure operational excellence, innovation, and continuous improvement across all technology functions. Serve as a trusted advisor and strategic partner to executive leadership, guiding technology investment decisions and ensuring they align with enterprise value and long-term organizational success.
MINIMUM QUALIFICATIONS
Strong knowledge of technology and healthcare operations as normally obtained through the completion of a Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field required.
Fifteen years of progressive leadership experience in AI, data, or software engineering disciplines, including 5+ years in executive-level roles within healthcare, health tech, or other regulated industries.
Proven success leading enterprise AI strategy, large-scale data platforms, and infrastructure modernization. Demonstrated ability to lead large teams, manage multimillion-dollar budgets, and deliver measurable ROI. Deep expertise in cloud, data architecture, AI platforms, and IT operations. Strong understanding of clinical, operational, and digital workflows within healthcare. Experience implementing responsible AI frameworks and enterprise data governance. Success in managing multimillion-dollar technology portfolios and delivering measurable performance improvement.
Visionary, pragmatic, and collaborative leader who drives transformation through influence and alignment. Committed to fostering innovation, accountability, and a people-first technology culture. Skilled communicator who can engage effectively with C-suite executives, clinicians, and operational leaders.
PREFERRED QUALIFICATIONS
Master’s degree (MBA, MSCS, or equivalent) preferred.
Additional related education and/or experience preferred.
EEO Statement:
EEO/Disabled/Veterans
Our organization supports a drug-free work environment.
Privacy Policy:
Privacy Policy
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Banner Health, 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 $154,000 based on 8,743 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $259,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Banner Health AI Hiring
Banner Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US.
Location Context
Across all AI roles, 16% (615 positions) offer remote work, while 3,251 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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