Applied research scientist and AI/ML engineer specializing in generative AI agents, LLM fine-tuning, RAG systems, forecasting, optimization, and enterprise supply chain applications.
Architected and led development of NAVI SCDP Modeler Assistant, a neurosymbolic agentic AI system for enterprise supply chain design. Designed multi-agent orchestration using LangGraph, MCP, and Pydantic; built hybrid dense and BM25 RAG systems; fine-tuned open-source LLMs using LoRA/QLoRA, DPO, and RFT; and established enterprise AI CI/CD and observability across AWS. Mentored scientists and drove technical hiring.
Architected and scaled Coupa Demand Modeler for enterprise clients with more than one million time-series SKU/location combinations. Built a production forecasting engine using Python and PySpark with macroeconomic data ingestion, feature engineering, feature selection, and expanding-window backtesting across deep learning, machine learning, and classical forecasting models.
Developed probabilistic forecasting and global cross-learning architectures for new product introductions, along with explainable AI capabilities for safety-stock and service-level optimization.
Developed production-grade probabilistic forecasting models using machine learning and external data features to optimize inventory decisions for a Fortune 500 CPG client's Amazon sales channel.
Developed production-grade probabilistic forecasting models using machine learning and external data features to optimize inventory decisions for a Fortune 500 CPG client's Amazon sales channel.
Master of Science, Analytics
GPA: 4.00/4.00
Diploma (MSc. & BSc.), Electrical and Computer Engineering
GPA: 7.98/10.0; Top 5%
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