Vontario Taylor

Led the architecture and deployment of the MedLLAMA application on GCP, achieving a 20% improvement in model accuracy for healthcare queries. Designed a scalable ETL pipeline, reducing data processing time by 30% while ensuring HIPAA compliance. Enhanced system uptime by 25% through real-time data updates and continuous model monitoring.

Developed high-accuracy image annotation processes and a scalable ASR system, achieving 99.9% transcription accuracy. Engineered a Reading Agent Application for OCR and speech synthesis, improving user satisfaction by 25%. Enhanced text-to-image generation systems with Stable Diffusion, improving retail image classification accuracy.

Built a financial risk assessment platform using LASSO-LSTM networks, improving risk prediction accuracy by 15%. Developed an end-to-end MLOps framework, streamlining model development and deployment with CI/CD pipelines and real-time monitoring.

Developed a real-time travel booking recommendation engine using BERT, improving accuracy by 22%. Maintained a scalable RESTful API with 99.9% uptime and implemented Dockerized microservices, reducing deployment times by 50%. Mentored junior engineers in Python best practices.

Developed a customer management system using Flask and PostgreSQL, enhancing user experience. Analyzed client data with Pandas and NumPy to generate actionable insights for product development.

Enhanced Amazon's website SEO using Google Analytics and SEMrush. Implemented strategies to improve keyword targeting and page load speed, contributing to increased organic search rankings.

Grade: 3.8

Completed a Bachelor of Science in Computer Science with a GPA of 3.8. The program provided a strong foundation in programming, algorithms, and software development, preparing for a career in technology and machine learning.