Assistant Vice President of Artificial Intelligence

NYC Health + Hospitals is the largest public health care system in the United States. We provide essential outpatient, inpatient and home-based services to more than one million New Yorkers every year across the city's five boroughs. Our large health system consists of ambulatory centers, acute care centers, post-acute care/long-term care, rehabilitation programs, Home Care, and Correctional Health Services. Our diverse workforce is uniquely focused on empowering New Yorkers.

At NYC Health + Hospitals, our mission is to deliver high quality care health services, without exception. Every employee takes a person-centered approach that exemplifies the ICARE values (Integrity, Compassion, Accountability, Respect, and Excellence) through empathic communication and partnerships between all persons.

Work Shifts

9:00 A.M - 5:00 P.M

Duties & Responsibilities

Purpose of Functional Assignment:
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.

Essential Duties and Responsibilities:

1. Leads AI engineers, data scientists, and Machine Learning professionals, ensuring technical excellence and
consistent delivery of scalable AI solutions.
2. Defines and executes AI engineering roadmaps in partnership with senior leadership and aligns them with
organizational goals.
3. Oversees the full lifecycle of AI/ML systems - from design and prototyping to deployment, monitoring, and related
work.
4. Guides architecture decisions for AI platforms, data pipelines and model serving infrastructure; evaluates and
integrates new AI tools and frameworks as needed.
5. Establishes and maintains best practices for model development, code quality, version control, model lifecycle
management, and Machine Learning.
6. Develops and maintains collaborations with academic institutions, research organizations, and industry partners to
advance AI capabilities.
7. 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.
8. 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
1. 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 records of delivering enterprise-grade AI solutions;

or
2. A satisfactorily equivalent combination of education, training, and experience. However, all candidates must have a minimum of a Bachelor's Degree in disciplines listed in "1" above, or in a related discipline, and preference will be given to applicants with a doctorate degree.

Preferred

Certifications:

1. Google Cloud Professional Machine Learning Engineer.
2. AWS Certified Machine Learning - Specialty.
3. Microsoft Certified:
Azure AI Engineer Associate.
4. Certified Kubernetes Administrator.
5. Tensor Flow Developer Certificate.
6. HL7 FHIR Proficiency Certification.
7. Databricks Certified Professional Data Engineer.

Preferred Knowledge Areas, Skills, Abilities, and other

Qualifications:

1. Proven expertise in machine learning, deep learning, generative AI, and AI system design and deployment.
2. Proficient in Python, Tensor Flow/PyTorch, cloud services (AWS, Azure, GCP), and MLOps tools.
3. Strong knowledge of Machine Learning practices, including model versioning, CI/CD for Machine Learning, and production monitoring.
4. Experience deploying large-scale AI systems in production environments.
5. Programming Languages & Frameworks:
Python/R, Tensor Flow, PyTorch, Keras, Scikit-learn, XGBoost, Light

GBM.
6. Data Engineering & Big Data: SQL and No

SQL databases (e.g., Postgre

SQL, Mongo

DB), Apache Spark, Databricks, Airflow.
7. Cloud Platforms: AWS (e.g., Sage Maker, EC2, S3), Microsoft Azure (e.g., Azure ML, Databricks), Google Cloud Platform (e.g., Vertex AI, Big Query).
8. Machine Learning & Deployment:
Docker, Kubernetes, MLflow, Kubeflow, CI/CD tools (Git Hub Actions, Jenkins, Git Lab CI).
9. Version Control &

Collaboration:

Git, Git Hub, Git Lab, JIRA, Confluence.
10. Visualization & BI Tools:
Tableau, Power BI, Looker, Jupyter Notebooks, VS Code, PyCharm.
11. APIs & Integration:
FastAPI, Flask, and tools for secure model deployment and EHR system integration (where applicable).
12.…