Agentic AI Engineer · M.S. Artificial Intelligence · Retired U.S. Army Special Forces Medic
I build AI applications that connect models to tools, data, and the people making decisions. My work spans multi-agent orchestration, retrieval, session memory, and evaluation—from early prototypes to production workflows.
Portfolio · LinkedIn · Cleland Co · Email
- Agentic applications: tool-calling loops, multi-agent workflows, MCP integrations, and session memory.
- Retrieval and decision support: systems that turn documents and external data into useful, reviewable outputs.
- Evaluation and delivery: prompt evaluation, model assessment, APIs, and reproducible ML pipelines.
Previously, as Medical Innovation Officer at VetClaims.ai (August 2025–May 2026), I built agentic AI and clinical-evidence workflows and owned initiatives from prototype through delivery. That proprietary work is described here at a high level; the repositories below showcase my public ML and software projects.
A collaborative clinical ML project covering preprocessing, feature engineering, class-imbalance handling, and model evaluation. Includes patient-level data splits, model cards, and a model registry to support reproducible experiments.
Focus: clinical ML · evaluation · reproducibility
An image-to-sequence project that converts mathematical expressions into LaTeX using a CNN or ResNet encoder and an LSTM decoder.
Focus: deep learning · computer vision · sequence modeling
An image-classification project using a ResNet18 augmented with a Convolutional Block Attention Module (CBAM) to identify plant diseases.
Focus: computer vision · attention mechanisms · image classification
An academic project exploring collaborative parking allocation through game theory, A* pathfinding, demand forecasting, and driver-behavior modeling.
Focus: optimization · search algorithms · simulation
| Area | Tools and techniques |
|---|---|
| Languages | Python, TypeScript, JavaScript, SQL |
| Agentic AI | Tool calling, MCP, RAG, multi-agent orchestration, prompt evaluation |
| Models and ML | OpenAI, AWS Bedrock, Hugging Face, PyTorch, scikit-learn, XGBoost |
| Delivery | FastAPI, Docker, MLflow, Git, CI/CD |
I earned my M.S. in Artificial Intelligence, with a concentration in Machine Learning, from the University of Michigan–Dearborn (2025). Before moving into software and AI, I served as a U.S. Army Special Forces Medical Sergeant, retiring in 2022.
That experience shapes my engineering priorities: make the reasoning inspectable, understand where a system can fail, and give people useful information when decisions matter.
Based in Essexville, Michigan. Open to agentic AI and applied AI engineering opportunities that are remote or based in Michigan.
Have a role or project in mind? Get in touch.