01Experimental public agent project
Incident Lab
Built a local incident investigator with real MCP tool calls, evidence-linked proposals, approval-gated simulation, and inspectable recorded evaluations.
Jeremy Cleland · AI engineering
Open to opportunitiesAgentic AI
Engineer.
I build AI applications with tool calling, retrieval, and evaluation.
Agents / Retrieval / EvaluationPython · FastAPI · PyTorch
01 / Selected work
From complex workflows
to working systems.
Read case study : Incident Lab
02Production agentic systems
Agentic Claims & Benefits Workflows
Built multi-agent and tool-calling workflows for clinical evidence and veteran benefits, with human review for consequential decisions.
03Academic / computer vision
PlantDoc: Plant Disease Classification
Developed a plant-disease classifier using an attention-augmented ResNet18 and inspected model behavior with evaluation visualizations.
02 / Engineering approach
Build for the people
who depend on it.
01
Understand the workflow
Work with domain experts to define the task, the evidence, and where human judgment belongs.
02
Connect the right tools
Build retrieval and tool-calling workflows with clear interfaces and deliberate review points.
03
Evaluate and improve
Use repeatable test cases, inspect failures, and iterate on reliability through actual use.
03 / Background
Engineering shaped
by responsibility.
I developed agentic systems for clinical-evidence and benefits workflows at VetClaims.ai, and ML pipelines as a graduate research assistant at the University of Michigan-Dearborn.
I completed my M.S. in Artificial Intelligence in 2025. I retired from the U.S. Army in 2022 after serving as a Special Forces Medical Sergeant.
More about my background →Let’s work together
Useful AI starts with
a real problem.
Based in Michigan. Open to remote roles from Michigan and Michigan-based opportunities.