Engineering Leader in AI/ML driven Quality Assurance

Project focus is to advance validation and governance of large-scale data, machine learning and generative AI systems within regulated environments. This project defines a set of best practices for end-to-end quality assurance of AI driven systems to be accurate, explainable, compliant with regulations, and ethical when processing PII/PHI information. Validation scope includes predictive analytics, machine learning, AI assisted decision making and generative AI chatbots for use in areas such as healthcare enrollment, payment integrity, fraud detection and operational analytics. Risk aware validation methods will be introduced to generative AI to prevent data leaks, reduce hallucinations, restrict access and provide an auditable record of activity. The project integrates data quality validation, model behavior testing, explainability verification and post deployment monitoring to move from AI functional testing to defensible and risk aware validation. The results of these processes have produced measurable outcomes; increased system reliability, improved audit readiness, increased trust among stakeholders and significant monetary recoveries. Ultimately, this project illustrates how discipline in engineering, robust validation and responsible AI practices can allow organizations to implement powerful AI technologies securely, transparently, and at scale while maintaining regulatory compliance and public trust.