The National Science Foundation (NSF) awarded a $597,292 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the Research Foundation of the City University of New York (RFCUNY) - Hunter College to improve the long-term reliability and evolvability of machine learning (ML) systems. This 3-year project will develop methodologies and automated refactoring techniques to address technical debt in ML systems, which can negatively impact the effectiveness and societal impact of artificial intelligence. The research aims to advance software engineering concepts in ML practice, promote the democratization of the AI workforce, and increase U.S. economic competitiveness. Key project activities include mining code and data for manual refactorings, formulating methodologies and automated refactorings for improving object-orientation and simplifying complex ML models, and designing a novel research infrastructure for evaluating and integrating refactoring capabilities.