Sr. Data Science & AI Engineer

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. 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 AI and Technical Leadership Implement controls for drift detection, fairness assessment, explainability, hallucination detection, validation, and regulatory alignment in deployed AI systems. Establish coding, review, testing, documentation, and deployment standards for AI engineering across teams. Mentor developers and lead architecture decisions for reusable pipelines, APIs, services, and deployment patterns. Contribute to institutional AI governance, technical training, and enterprise enablement of responsible AI adoption. Minimum Qualifications To qualify you must have a 5+ years of hands‑on experience designing, deploying, and supporting AI/ML systems in production environments, in healthcare, life sciences, or other regulated settings. Demonstrated experience with enterprise MLOps practices including model registry management, CI/CD for ML, monitoring, observability, retraining workflows, and operational governance. Proven experience building and deploying NLP, LLM, agentic AI, or other applied AI systems for clinical text understanding, knowledge extraction, phenotyping, clinical workflow support, or real‑world evidence applications. Experience with healthcare data platforms including clinical data warehouses, OMOP-CDM or similar common data models, and EHR‑derived data assets. Strong experience with cloud services and platform engineering on Azure and AWS, including secure data services, compute orchestration, storage patterns, and deployment automation. Experience implementing scalable data and ML pipelines using Databricks, Apache Spark, and data lake or lakehouse architectures. Experience with containerized application and model deployment using Docker, Kubernetes, and workflow orchestration frameworks. Experience deploying predictive models, CDS‑oriented services, trial matching solutions, or patient screening workflows integrated with Epic or similar EHR platforms is strongly preferred. Demonstrated leadership mentoring technical staff, conducting code and model reviews, defining engineering standards, and guiding architecture decisions across multiple stakeholders. Expert proficiency in Python for data engineering, machine learning engineering, API development, automation, and platform integration. Strong proficiency in SQL and experience with relational, analytical, and NoSQL data platforms. Extensive hands‑on experience with AI‑assisted development tools such as GitHub Copilot, OpenAI Codex, Claude Code, or similar coding copilots for engineering acceleration and quality improvement. Strong command of MLOps and ML platform technologies such as MLflow, Kubeflow, Azure Machine Learning, AWS SageMaker, Databricks ML, or equivalent frameworks. Experience with CI/CD platforms and engineering workflows including GitHub Actions, Azure DevOps, GitLab CI/CD, Jenkins, or comparable systems. Proficiency with orchestration and pipeline frameworks such as Apache Airflow, Prefect, Dagster, Argo Workflows, or similar tools. Solid understanding of containerization and scalable deployment frameworks including Docker, Kubernetes, Helm, and infrastructure‑as‑code approaches. Experience with Apache Spark, distributed data processing, and large‑scale data engineering patterns for model training and feature generation. Experience with Databricks for collaborative analytics, scalable ETL, feature engineering, ML experimentation, and production deployment workflows. Proficiency with machine learning and deep learning frameworks such as scikit‑learn, XGBoost, LightGBM, PyTorch, TensorFlow, or JAX. Experience with NLP, LLM, and agentic AI toolchains such as Hugging Face Transformers, spaCy, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, vector databases, prompt and evaluation frameworks, and retrieval‑augmented generation patterns. Knowledge of healthcare data standards and models including OMOP Common Data Model, HL7 FHIR, SNOMED CT, LOINC, RxNorm, and related semantic assets. Experience with version control, collaborative software development, testing, and release management practices in team environments. Preferred Qualifications Experience integrating with Epic, FHIR APIs, HL7 interfaces, SMART on FHIR applications, Epic CDS workflows, BestPractice Advisories, In Basket, or related clinical systems. Familiarity with feature stores, online/offline feature serving patterns, and real‑time inference infrastructure. Experience with multimodal or foundation models spanning text, imaging, genomics, and structured clinical data. Experience with GPU orchestration, distributed training frameworks, and model optimization for scale and cost control. Knowledge of causal inference, reinforcement learning, human‑in‑the‑loop systems, and adaptive trial or learning health system methodologies. Experience with semantic web technologies, RDF/SPARQL, graph databases, Neo4j, or biomedical knowledge graph implementation. Epic integration patterns, FHIR APIs, HL7 interfaces, SMART on FHIR, or Epic CDS workflows. Multimodal modeling across text, imaging, genomics, and structured clinical data. Knowledge graphs, semantic web technologies, and biomedical ontology implementation. Causal inference, reinforcement learning, or human‑in‑the‑loop decision systems. Strong knowledge of fairness, subgroup evaluation, explainability, drift monitoring, and validation in healthcare AI. Understanding of HIPAA, privacy, security, and applicable FDA considerations for clinical AI deployment. Ability to translate clinical and operational requirements into scalable technical designs. Strong communication, documentation, and cross‑functional collaboration skills. Commitment to code quality, testing, observability, and operational reliability. NYU Grossman School of Medicine is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. Salary range for the role is $101,493.51 - $147,000.00 annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses, incentive, differential pay or other forms of compensation or benefits. To view the Pay Transparency Notice, please refer to the notice included in the job posting. #J-18808-Ljbffr