Software engineer with experience building pharmacy document and claims systems using React, Node.js, Java Spring Boot, and secure partner integrations. I improve performance with caching (Redis), ensure reliability with automated testing (JUnit/Jest), and deliver CI/CD workflows using GitHub Actions.

Jared Stinson

Software engineer with experience building pharmacy document and claims systems using React, Node.js, Java Spring Boot, and secure partner integrations. I improve performance with caching (Redis), ensure reliability with automated testing (JUnit/Jest), and deliver CI/CD workflows using GitHub Actions.

Available to hire

Software engineer with experience building pharmacy document and claims systems using React, Node.js, Java Spring Boot, and secure partner integrations. I improve performance with caching (Redis), ensure reliability with automated testing (JUnit/Jest), and deliver CI/CD workflows using GitHub Actions.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
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Language

Javanese
Advanced

Work Experience

Associate Software Engineer at Optum, Inc.
July 1, 2022 - March 31, 2026
Developed a pharmacy document viewer using React and Node.js with editable annotations, positionable text overlays, document rendering, and persistent storage workflows used by 200+ reviewers. Delivered a Spring Boot microservice in Java to process pharmacy insurance claims compliant with NCPDP standards, securely routing encrypted transactions via TDP protocol between external partners and internal systems. Reduced average API response times by integrating Redis to cache document state server-side, eliminating redundant API calls on page refresh and application restart. Wrote unit and integration tests with JUnit for the claims microservice and Jest for the document viewer, raising coverage to 80%+ and preventing regressions in the CI pipeline. Built REST APIs for a directory microservice serving usernames, permissions, and routing to other internal microproducts. Implemented CI/CD pipelines with GitHub Actions to automate testing, builds, and deployments. Validated REST endpoints usi
Machine Learning Researcher at UAB School of Medicine
April 1, 2021 - April 30, 2022
Developed machine learning models to predict defibrillation success using pre-shock ventricular fibrillation waveform data from cardiac arrest patients. Trained convolutional neural networks in Python and MATLAB and evaluated cross-domain generalization by applying models to porcine cardiac datasets. Achieved 85% accuracy and 83% sensitivity via cross-validated evaluation and hyperparameter optimization. Produced research findings supporting clinical decision-making between defibrillation and CPR interventions and recommended protocol optimization. Presented results to a faculty review committee.

Education

Master of Science in Computer Science at Georgia Institute of Technology
January 1, 2026 - December 31, 2026
Bachelor of Science in Biomedical Engineering at University of Alabama at Birmingham
January 1, 2022 - December 31, 2022

Qualifications

Add your qualifications or awards here.

Industry Experience

Healthcare, Software & Internet, Computers & Electronics