Engineer II, Gen AI

About the role

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.

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