Applied AI Health Data System Engineer-Senior Manager

What you'll do

• Oversee the development of healthcare AI and GenAI solutions, including clinical use case design and RAG pipeline development.
• Lead large healthcare data science engagements to innovate delivery processes and drive continuous improvement across use case lifecycles.
• Manage the daily operations of a global healthcare data science team, overseeing model development, MLOps practices, and model governance.
• Architect and build production-grade RAG pipelines, MCP connections, agentic AI workflows, and MLOps frameworks.
• Guide teams in processing structured and unstructured healthcare data, such as clinical notes and claims, for LLM-powered solutions.
• Engage with health system clinical, financial, and operational leaders to align AI initiatives with organizational priorities.
• Develop and present healthcare AI proof of concepts and production use cases for clinical decision support and operational optimization.
• Coach and manage associates by providing feedback and guiding their development in healthcare domain knowledge and technical AI capabilities.

What we're looking for

• 12 years of experience with exposure to healthcare data science, health IT, or AI solution development for health system clients.
• At least 6-7 years of experience at a health system.
• Domain expertise in the healthcare value chain including Claims, Pharmacy, Finance, and Clinical Domains (preferred).
• Experience managing development teams building healthcare AI and GenAI solutions using Python, analytical modeling, and prompt engineering (preferred).
• Proficiency with LLM frameworks (LangChain, LlamaIndex), relational/non-relational storage, and cloud platforms like Azure, AWS, or Google Cloud (preferred).
• Experience with MLOps practices, Git version control, and CI/CD in regulated, PHI-handling environments (preferred).
• Ability to travel up to 80% of the time.
• Bachelor's Degree.