Sr. AI Engineer Job New York New York USA,IT/Tech

Location: New York

Position Summary

We have an exciting opportunity to join our team as a Sr. AI Engineer. In this role, the successful AI Engineer will design, implement, and operate production‑grade Generative AI and Machine Learning solutions that support NYU Langone Health’s 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.

Job 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.

Perform other duties as assigned.

Minimum Qualifications

Bachelor’s degree in computer science, software engineering, or a related field.
At least 1‑3 years of hands‑on 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 Tensor Flow.
Experience 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,…