Assistant Vice President of Artificial Intelligence

About the Role

Under direction of the Vice President/Chief Data and Artificial Intelligence Officer, implements Artificial Intelligence (AI) to enhance high-quality care, optimize workflows, and ensure equity in healthcare delivery. Manages engineering teams, sets technical direction, and works collaboratively with cross-functional partners to translate AI research into real-world, production-grade AI solutions that deliver measurable improvements in patient care, financial and/or operational efficiency.

Responsibilities

• Leads AI engineers, data scientists, and Machine Learning professionals, ensuring technical excellence and consistent delivery of scalable AI solutions.
• Defines and executes AI engineering roadmaps in partnership with senior leadership and aligns them with organizational goals.
• Oversees the full lifecycle of AI/ML systems — from design and prototyping to deployment, monitoring, and related work.
• Guides architecture decisions for AI platforms, data pipelines and model serving infrastructure; evaluates and integrates new AI tools and frameworks as needed.
• Establishes and maintains best practices for model development, code quality, version control, model lifecycle management, and Machine Learning.
• Develops and maintains collaborations with academic institutions, research organizations, and industry partners to advance AI capabilities.
• May contribute to peer-reviewed publications, presents at conferences, and stays engaged with the AI research community to keep the System at the forefront of innovation.
• Collaborates with product, data, and infrastructure teams to align AI capabilities with enterprise needs; acts as a trusted technical advisor to the System's stakeholders and non-technical partners.

Minimum Qualifications

• Master's degree from an accredited college or university in Computer Science, Engineering, or related discipline; and eight (8) years of experience in software/AI engineering, four (4) years of which must have been in a leadership role managing cross-functional technical teams such as AI/ML engineers, data scientists, and Machine Learning professionals, and with a proven track record of delivering enterprise-grade AI solutions; or
• A satisfactorily equivalent combination of education, training, and experience. However, all candidates must have a minimum of a Bachelor's Degree in disciplines listed above, or in a related discipline, and preference will be given to applicants with a doctorate degree.

Preferred Certifications

• Google Cloud Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Certified Kubernetes Administrator
• TensorFlow Developer Certificate
• HL7 FHIR Proficiency Certification
• Databricks Certified Professional Data Engineer

Skills & Knowledge Areas

• Proven expertise in machine learning, deep learning, generative AI, and AI system design and deployment.
• Proficient in Python, TensorFlow/PyTorch, cloud services (AWS, Azure, GCP), and MLOps tools.
• Strong knowledge of Machine Learning practices, including model versioning, CI/CD for Machine Learning, and production monitoring.
• Experience deploying large-scale AI systems in production environments.
• Programming Languages & Frameworks: Python/R, TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM
• Data Engineering & Big Data: SQL and NoSQL databases (e.g., PostgreSQL, MongoDB), Apache Spark, Databricks, Airflow
• Cloud Platforms: AWS (e.g., SageMaker, EC2, S3), Microsoft Azure (e.g., Azure ML, Databricks), Google Cloud Platform (e.g., Vertex AI, BigQuery)
• Machine Learning & Deployment: Docker, Kubernetes, MLflow, Kubeflow, CI/CD tools (GitHub Actions, Jenkins, GitLab CI)
• Version Control & Collaboration: Git, GitHub, GitLab, JIRA, Confluence
• Visualization & BI Tools: Tableau, Power BI, Looker, Jupyter Notebooks, VS Code, PyCharm
• APIs & Integration: FastAPI, Flask, and tools for secure model deployment and EHR system integration (where applicable)
• Proven track record of academic engagement, including collaborations, publications, or conference presentations in AI/ML fields.

Equipment Operated

General office equipment (e.g., computer, phones, scanner, copier)