In a pioneering effort to elevate vehicle safety and quality control, I led the development of an AI-driven anomaly detection system focused on automating inspections of airbags and batteries. This project showcased the effective use of cutting-edge technology to ensure the highest standards of safety and reliability in automotive manufacturing. Key contributions include:Advanced AI Implementation: Employed leading-edge tools such as Argo, Apache Beam, MLFlow, and Nvidia Triton for the scalable training, tracking, and serving of AI models. This approach ensured optimal performance and efficiency in anomaly detection processes.Machine Learning Model Productionization: Successfully productionized machine learning models for the inspection of stamped parts. This involved the integration of REST-based services and deployment on high-performance compute Kubernetes clusters, showcasing a seamless blend of software engineering and machine learning expertise.Containerized Service Deployment: Expertly built and deployed containerized machine learning services utilizing a robust stack of technologies, including Microsoft Azure, Docker, and Kubernetes. This initiative significantly enhanced the deployment efficiency and scalability of our ML solutions.

Leveraged expertise in software engineering to revolutionize a critical manufacturing application for General Motors, significantly enhancing the traceability of vehicle parts. Achieved this transformation by implementing state-of-the-art Continuous Integration/Continuous Deployment (CI/CD) practices, which streamlined build processes and dramatically reduced deployment times from over 10 hours to just 30 minutes. Key achievements include:Innovative Development: Spearheaded the creation of a Spring Boot-based system for processing vehicle part data, replacing outdated methods. This initiative not only fortified security and stability but also accelerated data processing times from upwards of 20 minutes to mere seconds, marking a pivotal advancement in operational efficiency.Operational Excellence: Drove a 30% reduction in manufacturing-related priority 1 incident tickets by automating crucial operational tasks, including server restarts, health checks, and error report generation. This strategic automation greatly minimized downtime and optimized system reliability.

During my tenure as a Software Developer at UTEP, I played a pivotal role in enhancing the educational and organizational infrastructure through the development and management of various web applications and databases. My responsibilities spanned the full spectrum of software development, from conceptualization to deployment, ensuring robust, efficient, and user-friendly solutions. Highlights of my contributions include:Web Application Development: Engineered and maintained multiple web applications designed to support courses and organizations within UTEP. This involved identifying requirements, designing solutions, and implementing these using modern web development practices.Technological Proficiency: Utilized a diverse set of tools and frameworks, including Ruby on Rails, Docker, Rancher, and PostgreSQL. My ability to leverage these technologies enabled the creation of scalable, secure, and high-performance web applications.Full Stack Development Expertise: Demonstrated comprehensive full stack development capabilities, encompassing database management, front-end development, and production-level deployment. My holistic approach ensured the delivery of end-to-end solutions that met the diverse needs of UTEP's academic and extracurricular programs.