Developed a data migration platform using Python, FastAPI and MySQL, engineering ETL pipelines to migrate and validate millions of GIS records, ensuring accuracy for federally mandated bridge asset submissions

Led technical alignment and code reviews for the engineering team, introducing automated CI/CD pipelines that improved deployment velocity and minimized production regressions by 20%

Integrated GPT-4o Mini to assist therapists using optimized prompt engineering, reducing caregiver response times by 25% and driving engagement through context-aware AI workflows

Engineered automated CI/CD pipelines and enforced TDD practices, achieving 90% unit/integration test coverage, resulting in stable releases with no critical production incidents during launch

Automated key research processes, reducing manual effort and improving data integrity.

Coordinated with faculty and researchers to customize solutions for diverse project needs.

Engineered and deployed scalable backend solutions using Node.js, optimizing server-side operations, and hosted on AWS for improved reliability and reduced latency.

Engineered a high-performance data consistency engine to validate over 100M records, implementing custom Python logic that reduced manual verification by 90% and ensured improved data validation reliability across global datasets

Shipped 3 core features in 6 months using TDD, achieving stable releases with no major production issues during initial rollout, while guiding technical delivery for 5 engineers on code maintainability and documentation standards