AI engineer focused on building practical, high-reliability systems across applied AI, automation, and intelligence-driven workflows.
I currently work at VetClaims.ai, where I build products and internal systems at the intersection of artificial intelligence, data, and operational execution.
My background combines:
- software and AI engineering
- military leadership
- medical decision-making in high-pressure environments
- product thinking for real-world operational use
That mix shapes how I approach engineering: build clearly, move fast, stay grounded, and make systems that are actually useful.
- Agentic AI systems
- LLM workflows and automation
- Intelligence and research platforms
- Applied machine learning
- Data infrastructure and backend systems
- Building tools for real operators, not just demos
Languages
- Python
- TypeScript / JavaScript
- SQL
AI / Data
- PyTorch
- Scikit-learn
- Pandas
- NumPy
- OpenAI APIs
Backend / Infra
- FastAPI
- PostgreSQL
- Docker
- Git
Exploring / Working With
- Neo4j
- Qdrant
- Retrieval systems
- Multi-agent orchestration
- Decision-support pipelines
Machine learning pipeline for early sepsis risk identification using structured clinical features, model evaluation, and optimization workflows.
Deep learning project for handwritten mathematical expression recognition and conversion to LaTeX.
Computer vision model for multi-class plant disease detection using convolutional neural networks and attention mechanisms.
This GitHub is where I keep:
- engineering projects
- AI/ML experiments
- prototypes
- research-driven builds
- tooling related to automation, intelligence, and decision support
Before moving full-time into AI and engineering, I served as a Special Forces Medical Sergeant. That experience still influences how I think about systems: reliability matters, clarity matters, and execution matters.
- Portfolio: clelandco.com
- LinkedIn: jeremy-cleland
I’m interested in building systems that are useful, resilient, and operationally real.