I architected LLM-powered intelligence systems, including meeting analytics pipelines that integrate with Microsoft Graph and AI transcription services to automatically generate structured records, sentiment insights, executive summaries, and follow-up actions for more than 100 client success meetings daily. I also built enterprise LLM reference agents leveraging Gemini Enterprise, vector databases, and RAG pipelines to consolidate internal knowledge and reduce operational mistakes.

Additionally, I modernized the internal task and project management architecture to enhance cross-team visibility, execution tracking, and governance—enabling faster delivery with clearer accountability and stronger operational insight.

To strengthen data reliability and governance, I directed a comprehensive internal data audit spanning nine subsidiaries, significantly improving data integrity, standardization, and trust in downstream reporting. At the same time, I managed and mentored a cross-functional team of six data scientists and data engineers—establishing Agile and Kanban delivery frameworks, implementing CI/CD pipelines in Azure DevOps, and introducing scalable engineering best practices to support long-term growth.

Building on this foundation, I guided the development of a predictive machine-learning model that identified nurse attrition risk, enabling proactive workforce planning and targeted retention strategies using Python and Scikit-learn.

In parallel, I produced comprehensive technical documentation for AR headset deployment, utilization, and maintenance—streamlining device integration, improving troubleshooting efficiency, and increasing overall user self-sufficiency. I also led structured group user testing for VR-based training modules centered on laboratory simulations and procedural workflows, leveraging participant feedback to refine the learning experience and measurably improve user satisfaction.

I implemented web scraping processes to retrieve and maintain data from Drexel College of Computing & Informatics systems—eliminating an estimated 40–60 hours of manual effort—and built a Selenium-powered web application to bulk delete course shells, reducing processing time by approximately 30 minutes per course. In addition, I authored comprehensive technical documentation to strengthen knowledge transfer for future technical assistants and resolved complex technical issues, including previously undocumented problems, achieving a 94% resolution rate without escalation.

I led a strategic overhaul of the underlying data pipeline—introducing structured logging, unit testing, and object-oriented development practices while converting a complex R-based model into Python—resulting in a 21% reduction in execution time. To support secure and scalable operations, I leveraged AWS infrastructure and Bitbucket repositories for daily data storage, management, and retrieval. Throughout the project lifecycle, I communicated progress, technical insights, and performance outcomes to department leadership through monthly sprint review presentations.

This included engineering automated credential creation workflows that significantly accelerated provisioning while reducing manual administrative effort, as well as building an onboarding monitoring application that saved approximately one hour and forty-five minutes of processing time each week. I also developed an automated monthly termination reporting solution that reduced reporting time from two hours to just twenty minutes, improving operational efficiency and enabling more timely workforce insights.

To enhance usability and adoption, I designed and developed an intuitive front-end interface that streamlined database interaction and improved accessibility for non-technical users. I also produced comprehensive technical documentation and instructional video guides to support onboarding, training, and sustained system utilization. In addition, I implemented automated shipping label generation and printing workflows for large user segments and engineered a dependable bulk data import pipeline to successfully migrate and integrate legacy datasets into the new environment.