Over the past nine years, I have architected and deployed production-scale Large
Language Model systems, advanced Retrieval-Augmented Generation (RAG) platforms, and
fine-tuned LLMs using cutting-edge techniques including LoRA, QLoRA, and RLHF. My
expertise spans end-to-end LLM engineering—from data ingestion and vector
database optimization to model fine-tuning, agentic AI orchestration, and serverless deployment
architectures on AWS and GCP.
I hold a Ph.D. in Industrial Data Science and Engineering from Pusan National
University & Pukyong National University (Joint Degree, QS <500) with an outstanding CGPA
of 4.15/4.50. My dissertation pioneered machine learning approaches for
geological interpretation, while my current work at Philip Morris International
(PMI) focuses on consumer analytics, demand forecasting, and AI-powered commercial
intelligence at scale.
Professionally, I have nine+ years of diverse experience designing AI solution
architectures that reduce operational costs by up to 60% through intelligent LLM routing,
automated reasoning systems, and explainable AI frameworks. My work spans banking,
mining, transportation, semiconductor, manufacturing, pharmaceuticals, FMCG, media, and oil
& gas—delivering measurable business value through sophisticated AI
engineering and MLOps practices.
My research contributions include 20+ peer-reviewed publications in IEEE
Transactions, Journal of Applied Geophysics, and Transportation Research, with 200+
research citations, focusing on advanced deep learning, explainable AI, and novel
LLM applications. I've won multiple AI hackathons and received prestigious scholarships including
the NRF BK-21 South Korea Scholarship, demonstrating both theoretical rigor and practical
implementation excellence.
