Engineer II, Gen AI

NYU Langone Health•New York, NY

2d•$94,578 - $110,000

About The Position

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 an Engineer II. In this role, the AI Engineer will design, implement, and operate production-grade Generative AI and Machine Learning solutions that support NYU Langone Healths Remote Patient Monitoring (RPM) initiatives. You will work at the intersection of healthcare and technology to deploy, monitor, and optimize large language models and supporting services for real-time clinical workflows, patient engagement, and operational use cases. Partnering with data scientists, clinicians, care teams, and IT, you will bring practical, reliable, and compliant AI capabilities into the RPM platform and related systems.

Requirements

Bachelor's degree in Computer Science, Data Science, Biomedical Informatics, Software Engineering, or a related field.
At least 1-3 years of experience in AI Solution development
Strong programming skills in Python, or other languages commonly used in AI development.
Substantial knowledge of AI, machine learning, and deep learning
Experience with AI platforms like PyTorch or TensorFlow
Experience with building large-scale and/or compute-intensive applications on clusters for data engineering, model training and evaluation (HPC, Spark, Kubernetes)
Understanding of software development principles and methodologies, including data structures, data modeling and software architecture.
Excellent problem-solving skills and ability to work in a team environment.
Excellent communication skills, both verbal and written.

Nice To Haves

Masters degree in computer science, data science, biomedical informatics, software engineering, or a related quantitative discipline.
3-5 years of hands-on experience delivering production AI solutions, including LLM-based applications.
Experience with at least one major cloud platform (Azure, AWS) and cloud-native AI/ML toolchains; familiarity with CI/CD practices for AI applications.
Practical experience implementing retrieval-augmented generation (RAG), semantic search, and embedding models; knowledge of vector databases.
Experience with MLOps / LLMOps tooling (e.g., MLflow, Kubeflow, Airflow, Weights & Biases) for experiment tracking, model versioning, and monitoring/observability.
Experience building or maintaining AI-enabled healthcare applications, integrating with EHR systems, and operating within regulated environments (HIPAA); understanding of prompt engineering and fine-tuning methodologies; familiarity with LLM provider APIs (e.g., OpenAI, Anthropic, Azure OpenAI).

Responsibilities

Design and implement MLOps/LLMOps pipelines to deploy, monitor, and manage large language models in production healthcare environments, following software engineering best practices and team standards.
Collaborate with data scientists to deploy and/or fine-tune high-performing Generative AI models (e.g., for summarization, triage, patient messaging) and apply modern techniques from relevant published work where appropriate.
Develop scalable and robust data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and model deployment across the RPM ecosystem.
Implement monitoring and observability for AI applications, including tracking performance metrics, latency, model drift, safety indicators, and data quality; maintain model versioning and experiment tracking using tools such as MLflow or Kubeflow.
Evaluate and recommend AI tools and frameworks to meet clinical and operational requirements, including decisions around retrieval-augmented generation (RAG), vector databases, embedding models, and LLM providers, balancing compliance, performance, and cost.
Optimize inference performance and cost efficiency through techniques such as model quantization, batching, caching, and effective resource allocation; leverage containerization and orchestration tools (Docker, Kubernetes) for scalable, reproducible deployments.
Implement internal security and data protection standards in AI applications; ensure HIPAA compliance and adherence to institutional governance for PHI; assist with emerging AI risk, safety, and security controls.
Support the team in preparation for technical reviews and internal documentation (architecture, IT Security, AI), including design documents, runbooks, and operational procedures.
Collaborate with other team members and stakeholders to meet team objectives; partner with clinicians and product stakeholders to understand workflows, gather feature requirements, identify and document AI opportunities, create appropriate tickets, participate in backlog refinement, execute tickets, and engage in code-review activities.
Integrate CI/CD practices for AI applications to enable reliable, automated testing, deployment, and rollback in cloud environments.
Stay updated with the latest industry trends and advancements in Generative AI, LLMOps, and relevant cloud technologies; routinely share and demonstrate learnings with the team.
Provide technical guidance and coaching to less experienced team members; contribute to standards, reusable components, and best practices for AI development and operations.
Participate in all phases of the AI software development life cycle, including functional analysis, prototyping, development, evaluation, testing, deployment, refactoring, and technical support.
Performs other duties as assigned.