Position Summary Develop AI-driven solutions using machine learning, large language models, and agentic AI architectures to analyze data and support decision-making. Design predictive and generative models for applications that support analysis and agentic knowledge systems. Develop agentic AI systems capable of reasoning, planning, and interacting with data sources, APIs, and knowledge systems to support workflows. Key Responsibilities Apply natural language processing (NLP), LLMs, and retrieval-augmented generation (RAG) to extract insights from unstructured clinical documentation, including records, operative notes, and evaluations. Integrate and prepare data from multiple sources, including EHR systems such as EPIC. Build and maintain production AI pipelines, including model training, validation, deployment, and lifecycle management using modern MLOps practices. Implement AI governance and model monitoring, including performance tracking, bias detection, model drift monitoring, and explainability to support safe AI deployment. Develop APIs, services, and data pipelines that allow AI models and agent-based systems to integrate with existing hospital applications and analytics platforms. Create dashboards, visualizations, and analytical reports to communicate insights and model outputs to clinicians, operational leaders, and hospital leadership. Collaborate with clinicians, operational leaders, and researchers to identify opportunities where AI and advanced analytics can improve workflows, outcomes, and operational performance. Support research initiatives, publications, and innovation programs involving AI applications as needed. Experience Required Experience developing AI-driven solutions using machine learning, large language models, and agentic AI architectures. Experience designing predictive and generative models. Experience building agentic AI systems that reason, plan, and interact with data sources, APIs, and knowledge systems. Experience applying NLP, LLMs, and RAG to unstructured clinical documentation. Experience integrating data from multiple sources, including EHR systems. Experience building and maintaining production AI pipelines and using MLOps practices. Experience implementing AI governance, model monitoring, and safe deployment practices. Experience developing APIs, services, and data pipelines for integration with hospital systems. Experience creating dashboards, visualizations, and analytical reports for varied stakeholders. Experience collaborating across clinical, operational, and research teams. Preferred background in Computer Science, Engineering, Artificial Intelligence, Biomedical Informatics, or a related technical field.