Paul Okafor

Paul is a highly skilled AI/LLM Engineer and Data Scientist with over five years of experience designing and deploying intelligent, end-to-end AI systems. Currently pursuing a Ph.D. in Data Science and Analytics at the University of Oklahoma, Paul’s work bridges applied AI engineering and healthcare research, focusing on building interpretable, real-time AI systems for aging and disease modeling.

Paul’s technical strengths lie in LLM fine-tuning, multi-agent system design, and AI infrastructure development. He has built production-grade, multi-agent LLM platforms using LangGraph, LangChain, and Azure AI, including a flagship conversational AI agent for Telogical Systems, which became the company’s primary interface for telecom-data analytics. His independent portfolio extends to deep-research AI agents (“Morgana”), real-time voice AI systems integrating LiveKit and FastAPI, and computer vision models leveraging YOLO for object detection.

In academia, Paul’s research applies machine learning to understand the biological and cognitive impact of chemotherapy-induced aging in pediatric cancer survivors. His work explores predictive modeling, biological age estimation, and microvascular damage detection, contributing to emerging solutions for neurocognitive decline and age-related diseases. Beyond oncology, his research extends to Cerebral Small Vessel Diseases (CSVD) and federated models for biological age prediction, underscoring his commitment to AI for health and longevity.