Applied engineer. I put automation and AI into production healthcare workflows.
I turn complex operational data into systems people can trust.
Lead application engineer with 15+ years in enterprise healthcare, automation, and data-heavy workflows. I take messy operational systems, instrument them, and turn them into repeatable pipelines. Now applying that same approach to machine learning and generative AI. Ohio-based, remote for 10+ years, open to relocation for the right role.
Open to applied AI, data science, data engineering, platform and software engineering, automation, and forward-deployed or solutions roles.
Instrument the system, inspect the data, and make decisions from observable behavior.
Design for maintainability, reviewability, and the people who will operate the solution after launch.
Technical quality includes understanding the workflow, constraints, and consequences around the code.
SELECTED PROJECTS
Concrete work with scope labels that distinguish production delivery from personal lab work.
Applied AI workflow for clinical lab operations
Selected onto a cross-functional AI initiative. Designed an automated lab-resulting workflow that takes operational steps out of a manual path. Enterprise system, internals not public.
Enterprise Legacy Code Analysis & Instrumentation
Python automation and runtime telemetry for evidence-based modernization of a legacy Epic codebase.
Production system. Internals not public.
Determining which routines were active, obsolete, or safe to retire would have required months of manual code review.
Built a Python framework to analyze Epic M routines and programmatically insert execution tracking across the codebase, then used production metrics to distinguish observed activity from dormant code.
Converted code discovery into a repeatable, data-driven process and gave modernization decisions a stronger evidence base.
Provider Data Normalization Pipeline
An extraction and transformation workflow for cleaner, standardized enterprise imports.
Production system. Internals not public.
Provider records arrived with inconsistent formatting and required a largely manual cleanup process before they could be imported reliably.
Developed a Python workflow to extract source data, apply repeatable normalization rules, and produce standardized records for downstream Epic administration.
Replaced repetitive cleanup with an auditable transformation pipeline and improved consistency across the import dataset.
Enterprise Reporting Platform Migration
Programmatic migration of recurring reports after the retirement of a legacy platform.
Production system. Internals not public.
A large portfolio of enterprise reports needed to move to a new delivery model without recreating each configuration by hand.
Built Python automation to generate report configurations, XML definitions, and batch email delivery workflows from structured inputs.
Significantly reduced manual migration effort while creating a more consistent and repeatable configuration process.
Self-Hosted LLM Development Environment
A local environment for hands-on evaluation of open-source language models and AI-assisted workflows.
Understanding LLM tradeoffs requires direct experience with inference, model behavior, prompt design, privacy, and deployment constraints.
Designed and maintained an Ollama-based environment to evaluate Mistral and other open-source models, experiment with prompts, and prototype AI-assisted development and automation workflows.
Built practical intuition for the differences between local and cloud AI—including capability, latency, control, and operational complexity.
On-Device Computer Vision Application
A Kotlin Android application performing real-time object detection with local model inference.
Integrate a machine learning model into a responsive mobile camera workflow without relying on a remote inference service.
Combined TensorFlow Lite with Android’s Camera2 API to capture frames and run object detection directly on the device.
Demonstrated an end-to-end edge ML workflow with responsive inference, offline operation, and hands-on mobile model integration.
Course work: Clinical Outcome Prediction with NHANES. Supervised learning homework on public demographic, laboratory, and clinical data. No public repository.
EXPERIENCE & EDUCATION
A long enterprise engineering track record paired with focused graduate study in data science.
- 2019 — Present
Lead Application Engineer
TriHealthBuilt and automated application and data workflows, and migrated enterprise reporting, for healthcare operations that run on Epic. Selected onto a cross-functional AI initiative and built an automated lab-resulting workflow.
- 2016 — 2019
Senior Application Engineer
TriHealthBuilt governance reporting, clinical quality logic, electronic billing validation, and workflow automation across perioperative systems.
- 2011 — 2016
ServiceNow Developer + Programmer / System Analyst
Premier HealthDelivered ServiceNow workflows, Epic implementations, integrations, and SQL reporting for a 13,000+ employee healthcare system.
Master of Applied Science in Data Science
Illinois Institute of Technology
In progress
Bachelor’s Degree in Computer Science
Wright State University
2011
Epic-certified: Chronicles, Bridges, Cogito, Data Courier, OpTime Anesthesia, EpicCare Ambulatory.