• Design, train, and implement machine learning and AI models that support real-time decision-making. • Prepare, clean, and structure large-scale datasets in collaboration with data engineers using Snowflake and MongoDB. • Apply the Elastic Hierarchy framework to perform feature engineering and attribution across reconciled datasets. • Build and optimize agent-based workflows connecting ML models to front-end and client systems. • Create and manage JSON-based data payloads for model integration within enterprise workflows. • Integrate ML outputs into React-based front-end applications for visualization, analytics, and interactive use. • Deploy, monitor, and optimize ML models in production using AWS Lambda, EC2, and EKS/Kubernetes. • Ensure reproducibility, version control, and documentation across all ML development workflows. • Collaborate with cross-functional teams (product, data, and engineering) to deliver AI-powered product strategies. • Apply governance, auditability, and compliance standards for ML models used in regulated financial environments.