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Vice President for AI Healthcare Innovation
The Vice President (ED) of the UConn Health AI Institute will serve as the founding leader responsible for building and scaling a nationally recognized hub for AI\-driven healthcare innovation. The ED will translate UConn Health's strategic vision into operational, clinical, and financial success by positioning the Institute as a premier clinical proving ground for external AI companies, accelerating adoption of validated technologies, and generating long\-term institutional value through strategic investment. This role requires a unique combination of healthcare leadership, change management in legacy organizations, knowledge of venture investing, operational execution, and academic collaboration.
Key Responsibilities
1\. Strategic Leadership \& Institute Development
- Establish and operationalize the AI Institute in alignment with UConn Health's mission and strategic priorities
- Define and execute a market\-oriented strategy focused on identifying, evaluating, and integrating best\-in\-class external AI solutions rather than primarily incubating internal startups
- Position UConn Health as a trusted validation environment for AI solutions leveraging real\-world operational, and clinical workflows and patient data as appropriate.
- Build the Institute's national reputation as a leader in healthcare AI innovation
2\. Clinical \& Operational AI Integration
- Partner with clinical, operational, and administrative leaders to:
+ Identify high\-priority unmet needs across UConn Health
+ Define use cases for AI deployment
- Oversee structured pilots, validation studies, and enterprise\-scale deployment of AI solutions
- Ensure measurable improvements in:
+ Patient outcomes
+ Operational efficiency
+ Financial performance
3\. External Partnerships \& Ecosystem Development
- Develop a robust pipeline of AI companies for evaluation and partnership
- Serve as a strategic liaison between:
+ AI startups and UConn Health departments and stakeholders
+ UConn Health clinical leadership
+ Academic units (including the School of Business) as appropriate
- Establish UConn Health as a preferred partner for leading healthcare AI innovators
4\. Venture Fund \& Investment Collaboration
- Work in close partnership with the AI Institute Investment Committee, chaired by John Kim
- Support sourcing, evaluation, and validation of companies for potential investment
- Ensure that clinical pilots and operational results inform disciplined investment decisions
- Contribute to building a high\-performing venture portfolio that delivers long\-term financial returns to UConn Health
5\. Governance \& Institutional Alignment
- Report to UConn Health CEO within the UConn Health Finance Corporation
- Collaborate with the Institute's governing board, including external members and philanthropic partners
- Ensure transparency, accountability, and alignment with institutional priorities
6\. Financial Stewardship \& Sustainability
- Manage a dual\-structured model balancing:
+ Directly responsible for managing the operating budget
+ Provide strategic and operational support for the venture fund
- Drive progress toward a self\-sustaining financial model through:
+ Creating tangible revenue and expense efficiencies for UCONN Health
+ Returns from the venture investments
+ Licensing and commercialization opportunities
- Support ongoing fundraising and donor engagement efforts
7\. Team Building \& Organizational Leadership
- As the Institute evolves recruit and lead a multidisciplinary team across:
+ AI/technology evaluation
+ Clinical integration
+ Data science
+ Business development and operations
- Foster a culture of rigor, speed, accountability, and innovation
Key Qualifications
Required
- Advanced degree (PhD, MBA, MD or equivalent)
- 15\+ years of leadership experience in healthcare, technology, or innovation\-driven organizations
- Demonstrated experience in AI, digital health, or healthcare transformation initiatives
- Experience in healthcare systems, clinical workflows, and data environments
- Experience leading cross\-functional teams and complex implementations
Preferred
- Knowledge of venture capital, private equity, or corporate investment
- Experience in evaluating and scaling emerging technology companies
- Experience working in academic medical centers or research institutions
- Familiarity with regulatory, compliance, and ethical considerations in healthcare AI
Core Competencies
- Strategic vision with execution discipline
- Investment and commercial acumen (move to the last bullet)
- Change management leadership of large organizations
- Strong stakeholder engagement and partnership skills
- Data\-driven decision\-making
- Ability to operate at the intersection of academic medicine, industry, and capital markets
Success Metrics (Illustrative)
- Number and quality of AI solutions evaluated and deployed
- Measurable clinical and operational improvements
- External partnerships and national reputation of the Institute
- Progress toward financial sustainability
Why This Role Matters
This is a foundational leadership role with the opportunity to shape a first\-in\-kind model: an AI Institute that integrates clinical validation, operational transformation, and venture investment into a unified platform. The Executive Director will play a central role in advancing UConn Health's position as a leader in AI\-enabled healthcare innovation.
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 University of Connecticut, 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.
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.
University of Connecticut AI Hiring
University of Connecticut has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Farmington, CT, US.
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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