LLMs, RAG & Agentic AI
Prompting, semantic retrieval, tool orchestration, Graph RAG, LLM services, and multi-stage AI workflows.
AI ENGINEER • SYSTEMS ARCHITECT • DEFENSE TECHNOLOGIST
Building intelligent systems for complex, real-world missions.
I design and build AI-enabled systems that combine machine learning, large language models, knowledge graphs, distributed intelligent agents, and rigorous systems engineering. My work spans hands-on software development, technical architecture, and mission-focused innovation across Navy, DoD, and federal programs.
ABOUT
Engineering depth, operational context, and a bias toward useful systems.
I am a technical leader and hands-on AI practitioner with experience in machine learning, software development, systems engineering, data analytics, cloud architecture, and Navy/DoD programs. My recent work centers on agentic AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), hybrid knowledge graphs, and distributed intelligent-agent systems in Java and Python.
Earlier in my career, I led Navy C5I modernization policy at NAVSEA, directed advanced-technology programs supporting defense and federal customers, and served in a series of Navy combat systems, C4I, and systems-engineering roles. That combination continues to shape how I approach AI: technology matters, but only when it can be integrated into the mission, the data, the architecture, and the decision process.
EXPERTISE
Where I spend most of my time.
From model development through architecture and operational integration.
Knowledge Graphs
Graph-backed state, graph query generation, semantic retrieval, pgvector, hybrid structured/unstructured reasoning.
Machine Learning
Supervised and unsupervised learning, neural networks, TensorFlow/Keras, YOLO, NLP, anomaly detection, evaluation.
Autonomy & Simulation
Distributed agents, multi-agent behavior, swarm simulation, event-driven reasoning, mission-focused decision support.
Software & Data Engineering
Python, Java, SQL, Docker, relational data, data pipelines, vector databases, enterprise integration.
Defense Systems Engineering
NAVSEA, Navy C5I, RMF/NIST, interoperability, modernization, technical policy, Government demonstrations and reviews.
SELECTED WORK
Recent projects at the intersection of AI, graphs, autonomy, and defense.
AURORA AI / IDX
Graph-native agentic AI and mission-focused autonomy
Advanced AI engineering and architecture for Cougaar’s distributed intelligent-agent platform. Recent work includes Java-based graph reasoning, event-driven multi-agent simulation, graph-backed autonomous-agent state management, pgvector-enabled retrieval, and rapid Python prototyping of AI-enabled decision-support and swarm behaviors before transition into the distributed Java platform.
JavaPythonKnowledge GraphspgvectorMulti-Agent Systems
BATTLEFISH
Graph RAG for complex military specifications
Led development of a U.S. Army-sponsored capability for automated ingestion, decomposition, and operationalization of military communication specifications. Architected a Graph RAG approach combining structured and knowledge graphs with Hierarchical Task Networks to orchestrate multi-stage workflows and generate interoperable data models and message-processing logic.
Graph RAGHTNLLMsKnowledge Representation
DCAA
LLM-assisted review of contractual documents
Architected and developed an LLM proof of concept that automated review and question answering across sets of contractual documents, demonstrating how AI could augment auditors and reduce thousands of hours of repetitive document review.
LLMsDocument QAAzure MLGovernment AI
COMPUTER VISION
Real-time military aircraft identification
Developed a Python/YOLOv5 capability to detect, localize, and identify more than 40 military aircraft types in images and real-time video. The work was presented at the Military Operations Research Symposium and the NATO Operations Research Conference in 2023.
PythonYOLOv5Computer VisionReal-Time ML
CYBER / ML
Neural-network anomaly detection for industrial systems
Developed an unsupervised neural-network autoencoder in Python using TensorFlow and Keras to identify anomalous industrial-control-system behavior associated with cyber events.
TensorFlowKerasAutoencodersAnomaly Detection
BACKGROUND
Technical leadership grounded in Navy systems engineering.
Before focusing primarily on AI and machine learning, I spent much of my career engineering, integrating, modernizing, and managing complex defense information and combat systems.
2023–PresentCougaar Software
Senior AI Researcher / Senior Software Architect
2020–2025Defense Contract Audit Agency
Business Intelligence Program Manager
2006–2020Avineon
Director, Advanced Technology Division
2003–2006Naval Sea Systems Command
Navy C5I Modernization Policy Lead
1986–2003U.S. Navy
Combat systems, C4I, systems engineering, and technical leadership assignments
Ph.D.Electrical Engineering
Old Dominion University
M.S.Electrical Engineering
Naval Postgraduate School
B.S.Systems Engineering
United States Naval Academy
SELECTED PUBLICATIONS
Applied research across AI, autonomy, cyber, and systems engineering.
Autonomous Aircraft Identification
Military Operations Research Symposium; also presented at the NATO Operations Research Conference.
Implementation of a Neural Network Autoencoder to Detect Anomalous System Behavior
Advanced Machinery Technology Symposium.
Simulation of Swarm Behavior with Fuzzy Networks
MODSIM World.
Simulation of Cooperative Unmanned Systems Mission Execution Using Fuzzy Logic Networks
I/ITSEC.
CONTACT
Interested in AI, autonomy, mission systems, or applied R&D?
I’m always interested in technically challenging work where AI can improve real operational decisions.