Sr. Data Science & AI Engineer

1161457_RR00119626 Job ID: 1161457_RR00119626

NYU Grossman School of Medicine is one of the nation’s top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care. At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.

For more information, go to med.nyu.edu , and interact with us on LinkedIn , Glassdoor , Indeed , Facebook , Twitter and Instagram .

Position Summary We have an exciting opportunity to join our team as a Sr. Engineer I, AI.

NYU Langone Health seeks an experienced Data Science & AI Engineer to design, build, deploy, and govern enterprise-grade AI and machine learning capabilities that support clinical care, research, operational analytics, and learning health system initiatives across the institution. This role is situated within the Medical Center Information Technology (MCIT) and is focused on establishing durable institutional infrastructure rather than serving a single project or point solution. The position is intended for a technically strong engineer who can translate clinical and operational priorities into scalable AI systems, production-ready data products, and reusable platform capabilities aligned with enterprise standards and governance expectations.

This position combines advanced software engineering, applied machine learning, health data architecture, and technical leadership. The successful candidate will bring hands-on experience deploying models and AI services in production environments, with strong command of cloud infrastructure, CI/CD, orchestration, containerization, data lake and lakehouse patterns, and modern MLOps practices. Particular emphasis is placed on practical use of AI-assisted engineering tools such as GitHub Copilot, OpenAI Codex, Claude Code, and related platforms to accelerate high-quality development, improve documentation, and support disciplined code review and engineering productivity.

The ideal candidate will have demonstrated expertise in healthcare data environments including clinical data warehouses, OMOP Common Data Model, EHR integration, biomedical ontologies, semantic data models, knowledge graphs, and real-world evidence pipelines. This role also requires strong capability in NLP and LLM-based systems for clinical text understanding, phenotyping, knowledge extraction, and decision support, alongside deep familiarity with agentic AI patterns, trustworthy AI, model monitoring, fairness evaluation, explainability, and regulatory expectations for AI deployment in healthcare settings.

Job Responsibilities AI Engineering and Platform Delivery Build, deploy, and support production AI/ML, NLP, LLM, and agentic systems for clinical, research, and operational use cases.

Design scalable model deployment patterns, inference services, feature pipelines, and reusable AI components for enterprise use.

Implement MLOps standards for model lifecycle management, registry governance, monitoring, retraining, versioning, and release control.

Develop agentic workflows that combine LLM reasoning, retrieval, tool use, orchestration, memory, and human oversight within defined safety boundaries.

Health Data and Clinical Integration Engineer data pipelines across clinical data warehouses, OMOP-CDM, data lakes, and lakehouse platforms to support AI, analytics, and real-world evidence workflows.

Build multimodal pipelines that integrate structured EHR data, clinical text, imaging metadata, genomics, and other clinical data sources.

Deploy predictive models integrated with Epic and related EHR systems for clinical decision support, patient screening, clinical trial matching, cohort identification, and point-of-care risk prediction.

Partner with Clinical Informatics and engineering teams to integrate AI services into operational workflows with appropriate usability, reliability, and traceability.

**Trustworthy…