Interested in this AI/ML Engineer role at Beacon Health System?
Apply Now →About This Role
The Vice President of AI and Transformational Technology, reporting to the Chief Information and Digital Execution Officer (CIDEO), serves as Beacon Health System’s enterprise executive leader responsible for artificial intelligence and transformational technology as a core health system function, setting enterprise direction, establishing standards, and delivering measurable outcomes across all business units.
MISSION, VALUES and SERVICE GOALS
- MISSION: We deliver outstanding care, inspire health, and connect with heart.
- VALUES: Trust. Respect. Integrity. Compassion.
- SERVICE GOALS: Personally connect. Keep everyone informed. Be on their team.
- This role holds enterprise accountability equivalent to other Vice Presidents, with authority spanning strategy, governance, investment prioritization, and operational performance across artificial intelligence and transformational technology.
- The role owns the full AI lifecycle—from governance and ethics to deployment, workforce enablement, and performance measurement—with ultimate accountability for where and how AI is applied across the organization.
- Operating at the intersection of clinical operations, executive leadership, and information technology, the Vice President provides system\-wide leadership across all hospitals and business units, driving alignment beyond direct reporting structures.
- The role builds and leads a high\-performing, enterprise AI and transformation function and establishes scalable capabilities that reduce vendor dependency, accelerate time\-to\-value, and advance Beacon’s position in responsible AI adoption.
- The Vice President owns the enterprise AI portfolio—prioritizing investments, governing deployment, and tracking value realization in partnership with Finance and operational leaders.
- As a standing member of senior leadership forums, this role drives and owns enterprise AI and transformation strategy in partnership with executive leadership and is accountable for system\-wide value realization, risk posture, and adoption performance.
### Enterprise Scope \& Impact
- Transformational technology encompasses the platforms, partnerships, and capabilities that fundamentally redefine how work is performed across Beacon—spanning intelligent automation, Microsoft 365 and Power Platform, agentic and generative AI, partner innovation, and citizen\-developer enablement—distinct from traditional IT infrastructure.
- Accountable for enterprise\-wide AI and transformational technology, this role leads strategy, execution, and performance to drive adoption at scale and deliver measurable impact across all hospitals, service lines, and business units—enhancing workforce productivity, accelerating innovation, and enabling data\-driven decision\-making across the system.
- Direct influence over multi\-year capital and operating investments.
- Responsible for system\-level outcomes including financial performance, workforce productivity, patient experience, and clinical quality enabled by AI.
- Positions Beacon as a regional and national leader in responsible AI adoption.
Key Responsibilities
- Own and drive Beacon’s AI strategy including investment and roadmap sequencing.
- Serve as peer executive to clinical and operational leaders.
- Define metrics and report value realization to executive leadership and Board.
- Hold final authority on AI governance and investment prioritization.
- Accountable for enterprise AI portfolio performance and ROI.
- Lead enterprise AI governance, ethics, and compliance frameworks, including patient safety, equity, and bias oversight.
- Build and scale Microsoft 365 and automation capabilities.
- Oversee vendor AI ecosystem performance and lifecycle management.
- Drive enterprise AI literacy and adoption.
- Own AI investment portfolio with Finance partnership.
- Deliver system\-level financial impact including cost optimization and productivity gains.
Leadership Competencies
- Drives Results \- Consistently achieving results, even under tough circumstances.
- Customer Focus \- Building strong customer relationships and delivering customer\-centric solutions.
- Instills Trust \- Gaining the confidence and trust of others through honesty, integrity, and authenticity.
- Collaborates \- Building partnerships and working collaboratively with others to meet shared objectives.
- Communicates Effectively \- Developing and delivering multi\-mode communications that convey a clear understanding of the unique needs of different audiences.
ORGANIZATIONAL RESPONSIBILITIES
Associate complies with the following organizational requirements:
- Attends and participates in department meetings and is accountable for all information shared.
- Completes mandatory education, annual competencies and department specific education within established timeframes.
- Completes annual employee health requirements within established timeframes.
- Maintains license/certification, registration in good standing throughout fiscal year.
- Direct patient care providers are required to maintain current BCLS (CPR) and other certifications as required by position/department.
- Consistently utilizes appropriate universal precautions, protective equipment, and ergonomic techniques to protect patient and self.
- Adheres to regulatory agency requirements, survey process and compliance.
- Complies with established organization and department policies.
- Available to work overtime in addition to working additional or other shifts and schedules when required.
Commitment to Beacon's six\-point Operating System, referred to as The Beacon Way:
- Leverage innovation everywhere.
- Cultivate human talent.
- Embrace performance improvement.
- Build greatness through accountability.
- Use information to improve and advance.
- Communicate clearly and continuously.
Education \& Experience
- Master’s degree required; advanced clinical or technical degrees preferred.
- 12\+ years of progressive leadership experience in AI, digital transformation, or technology innovation.
- Demonstrated enterprise leadership experience and Board engagement.
- Proven ability to lead cross\-functional teams and drive measurable outcomes.
- Strong expertise in AI technologies, governance, and healthcare regulations.
Success Profile
===================
- Established enterprise AI operating model with executive adoption.
- Delivered measurable financial and operational AI value.
- Achieved broad workforce engagement and AI literacy.
- Positioned the organization as a leader in responsible AI.
Working Conditions
- Works in an office environment.
- May experience some mental/visual fatigue due to continued use of computer equipment.
Physical Demands
- Requires the physical ability and stamina to perform the essential functions of the position.
Location: Beacon Health System · Information Systems
Schedule: Full\-time, Day, Monday\-Friday 8:00\-5:00
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Beacon Health System, 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 $181,170 based on 12,692 positions with disclosed compensation.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Beacon Health System AI Hiring
Beacon Health System has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Granger, IN, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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