AI Engineer · Generative AI · Agentic Systems · RAG · Cloud Infrastructure
Dallas, North Carolina · U.S. citizen · Government and defense AI experience · Experience working with CUI
I build AI applications that turn complex documents, technical knowledge and business workflows into useful, reviewable outputs. My work spans discovery and architecture through hands-on development, deployment, customer demonstrations and iterative improvement.
At Invoke Inc., I work across government, defense and enterprise AI engagements. I translate requirements into working systems involving LLMs, tool-using agents, retrieval, document intelligence, multimodal processing and cloud deployment.
My approach combines model orchestration with the engineering around it: ingestion pipelines, APIs, user interfaces, persistent knowledge stores, evaluation and human review. I also support GitHub administration, CI/CD and reusable model-provider integrations.
| Area | Work |
|---|---|
| Government AI | Built and demonstrated a U.S. Air Force RAG and document-intelligence application supporting 100+ users, deployed in AWS GovCloud around CUI handling requirements |
| Agentic systems | Developed a SOCOM red-team AI platform with multi-agent coordination, simulation tooling and MCP integrations |
| Document intelligence | Built financial-document workflows processing 1,000+ heterogeneous documents, with source-linked extraction and human verification |
| Enterprise knowledge | Developed RAG, research-agent and RFP-generation workflows, including retrieval across 700+ engineering documents |
| Software modernization | Built Python and agentic tooling for COBOL-to-Java analysis and modernization using graph and vector representations |
| Technical enablement | Helped design and teach a two-week applied AI/LLM bootcamp for 40+ Airmen |
Read the experience page for more detail.
University of North Carolina at Charlotte
My master's thesis, AI Gait Restoration for Disabled Individuals, explored multimodal approaches to gait prediction and restoration. I developed software to synchronize EMG, gyroscope, accelerometer and camera data, then trained and compared machine-learning approaches.
As a research assistant, I developed an LSTM-based background-noise reduction pipeline for hearing aids and validated real-time inference on a Raspberry Pi 3B.
I maintain a Kubernetes and GitOps homelab with self-hosted services, distributed storage, backup automation and AI integrations. My projects also include BambuBuddy printer integrations, Ceph backup tooling and GPU fan control.
I am a volunteer firefighter with approximately two years of captain-level leadership experience, including team coordination, training and decisions in high-accountability environments.