Hamilton, Alabama, United States
Biochemist turned AI architect. BSc in Biochemistry & Molecular Biology. Upon graduation, I worked in a regulated quality testing lab in the following roles: Biochem Lab QC, QA & Systems Validation: - Real-time PCR/qPCR, DNA extraction, HPLC - Analytical method validation & troubleshooting - Microbiology QC testing & contamination control - Implemented corrective protocols that resolved persistent state compliance failures While working in testing labs, my focus shifted to AI technology, specifically exploring ways to integrate AI into FDA-regulated product development processes. That question became the foundation for everything I build today. I build neurochemical language models (NLMs) for pharmaceutical AI — traceable, auditable, GxP-ready. AI Model & Agent Design, Development, Integration & Training: - Neurochemical Language Model (NLM) architecture — patent pending - LLM fine-tuning, RAG pipelines, domain-specific AI agents - FDA AI/ML guidance integration (IEC 62304, CMMI Level 3) - Pharmaceutical data pipeline design (openFDA, DailyMed, PubChem, ClinicalTrials.gov) Founder, Nexus Concordat — AI infrastructure for pharma, biotech, and medical device product development. Our NLM platform delivers what standard LLMs cannot: interpretable reasoning, cryptographic traceability, and regulatory-grade auditability built into the architecture — not bolted on after the fact. https://www.linkedin.com/company/nexus-concordat/
Founder, Nexus Concordat I built an AI architecture that can do what no LLM can — prove how it reached its conclusion. The Neurochemical Language Model (NLM) generates an intrinsic 10D audit trail during processing, not reconstructed after the fact. Biologically-modeled state tracking. Pharmacokinetic decay. Reasoning provenance that satisfies GxP validation requirements. The FDA's January 2025 AI/ML guidance asked the industry for auditable, reproducible AI systems. We already built one. BSc Biochemistry & Molecular Biology. The same discipline that models drug metabolism models our AI's decision pathways. That's not a coincidence — it's the architecture. Patent Pending: 63/939,190 & 63/962,385 "The Architecture of the Digital Mind" CMMI Level 3 | IEC 62304 | 21 CFR Part 11 The industry is trying to explain black boxes after the fact. We solved it from inside the box.
AI/ML systems design and integration for biopharma regulatory workflows, medical device design control, and pharmaceutical software development. AI Architecture & Model Development: - Neurochemical Language Model (NLM) design — intrinsic audit trail generation for GxP-validated AI - Large Language Model (LLM) fine-tuning and domain-specific adaptation - Retrieval-Augmented Generation (RAG) pipeline engineering - Multi-agent AI orchestration and autonomous workflow systems - LoRA/QLoRA parameter-efficient training on constrained hardware Systems Integration & Deployment: - AI agent integration into drug development and preclinical discovery pipelines - Medical device software lifecycle integration (IEC 62304) - Automated pharmaceutical data ingestion (openFDA, DailyMed, ClinicalTrials.gov, PubChem) - CUDA-optimized model inference and production deployment - PostgreSQL, vector databases, and neurochemical state persistence Frameworks & Tools: - PyTorch, Transformers (HuggingFace), PEFT, LoRA - LangChain, RAG architectures, embedding models - Flask, FastAPI, nginx, systemd service management - Git, CI/CD, Docker, Red Hat / Linux administration - ONNX, quantization, model optimization for edge deployment
Various Lab positions, helped validate protocols and CMC, ISO ISO17025/A2LA CAP accreditions. My position was to verify our test results against the state provided samples to enusre accuracy in our testing protocols as well as setting up proper SOP and protocols. PCR/Microbiology/
• Sorghum genetics research: DNA extraction, PCR, gel electrophoresis, data analysis • Plant material collection, seed treatment, and preparation for agricultural research protocols