Reduced manual analysis time by over 60% while establishing reusable architecture patterns for enterprise GenAI adoption.
Designed and implemented a generative AI platform enabling finance and operations teams to interact with complex enterprise datasets using natural language. Architected a secure RAG-based system integrating structured financial data, semantic search, and governance controls to support AI-assisted decision making.
Enabled improved cost visibility and created a scalable AI-ready data foundation for predictive analytics across operational spending.
Architected a data and AI platform to classify operational spend across invoices, vendors, and cost allocations. Designed a lakehouse architecture with engineered AI features and classification models to support automated cost categorization and operational insights.
Reduced manual document processing effort by more than 60% while improving data accuracy and enabling centralized reporting.
Built an AI-powered document processing platform to automate ingestion, classification, and structured data extraction from operational and compliance documents. Integrated Azure AI services with automated pipelines, governance controls, and CI/CD deployment workflows.
Enabled reliable daily model predictions and improved operational insights across large-scale industrial systems.
Led modernization of a machine learning platform supporting predictive analytics across thousands of connected industrial devices. Architected an event-driven ML pipeline with model versioning, scalable inference services, and monitoring frameworks.
Provided organizations with scalable guardrails to safely operationalize generative AI across business units.
Developed enterprise architecture patterns and governance frameworks for responsible AI adoption, including RAG pipelines, agent orchestration, model evaluation frameworks, and security controls aligned with standards such as NIST AI RMF.