AI Researcher, Engineer, & Architect

I bridge cutting‑edge AI research and production‑scale deployment. As Co‑Founder & Chief Data Scientist of AI Squared, I lead the creation of custom LLM families, robust guardrails, and agentic orchestration pipelines. I hold a PhD in Explainable AI and a TS/SCI clearance (Full‑Scope Polygraph), enabling me to deliver mission‑critical capabilities for the DoD and Intelligence Community.

Duties at AI Squared

I am the Chief Data Scientist and Cofounder at AI Squared. In this role, I help lead the development of the company's AI platform and its browser-based integration technologies. I've led R&D initiatives to advance generative AI models, including creating custom LLMs and contributing to the BeyondML project, which was released as open-source to the Linux Foundation Data and AI.

Duties at Capitol Technology University

At Capitol Technology University, I serve as Adjunct Faculty, mentoring PhD students in AI research and teaching undergraduate courses like CS-150. My research focus includes generative AI, transformer models, and their applications to fields like agriculture. I also serve as an external examiner for dissertation defenses and support the development of doctoral research in AI.

Key Projects & Impact

Bolt LLM Family

Architected and delivered a suite of domain‑tuned LLMs with built‑in guardrails, on‑premise efficiency, and a modular API layer that powers AI Squared’s UNIFI platform.

BeyondML (Linux Foundation)

Open‑sourced a novel multi‑task neural architecture that isolates subnetworks for independent tasks—adopted by the Linux Foundation Data & AI community.

Agentic Orchestration Engine

Built a closed‑loop, tool‑use environment enabling LLMs to execute multi‑step workflows with dynamic skill extensions and secure system‑prompt configuration.

AI Squared UNIFI Platform

Delivered production‑grade APIs for embeddings, translation, chat, and a guardrails engine; supports on‑premise GPU clusters and HPC environments.

Technical Skills

  • AI/ML: LLMs, XAI, Agentic Frameworks, PEFT/LoRA, RLHF, BERT, RoBERTa, LSI, Topic Modeling
  • LLM Engineering: Quantization (GGUF, bitsandbytes), vLLM, TGI, Vector DBs, RAG, Advanced Chunking
  • Frameworks: PyTorch, HuggingFace, FastAPI, Dask, Scikit‑learn, Pandas, NumPy
  • Infra / MLOps: Docker, MLflow, W&B, GitHub Actions, DeepSpeed, Slurm, RabbitMQ, HPC, GPU acceleration
  • Languages: Python, JavaScript, C, LaTeX
  • Specializations: Explainability, Guardrails, Model Routing, Production RAG, LLM‑as‑Judge

Open‑Source Contributions

Over the years, I've contributed to and created multiple open-source projects. Some highlights include:

Education

I hold a PhD in Technology from Capitol Technology University, with a focus on Explainable AI. My dissertation explored linear regression feature engineering in classification trees. Additionally, I have a Master of Science in Business Analytics from the University of Maryland, and a Bachelor of Science in Mathematics from the same institution.

Selection of Talks and Publications