Chima Omike

“Built and deployed a compliance-sensitive LLM pipeline that extracts rebate logic from hospital–supplier medical contracts, using multi-layer redaction (regex/NER/dictionary), schema-validated structured outputs, and secure placeholder reinsertion. Hosted models on Amazon Bedrock to avoid retraining on sensitive data and improved both accuracy and cost by splitting the workflow into a lightweight section classifier plus a fine-tuned extraction model, orchestrated with LangChain and evaluated via layered, test-driven agent assessments.”

“Built and deployed a Unity-based, NSF-funded medical interaction system for the University of Houston MRI Lab, combining speech recognition, LLaMA-based function calling, and real-time simulation controls. Particularly strong in taking experimental AI systems from discovery through stabilization, with measurable impact including roughly 50% faster task completion than traditional point-and-click workflows.”