- Designed and implemented a data-driven web platform using React.js, Node.js, and MongoDB, enhancing the real-time tracking of Blue Light scanning data and improving MRB product analysis by automating defect resolution, leading to a 20% improvement in production efficiency.Spearheaded the migration to microservices architecture, leveraging Docker and Kubernetes for scalable deployments and Jenkins for fully automated CI/CD pipelines, which reduced system downtime and improved deployment frequency by 50%.- Developed GraphQL APIs to optimize internal application data querying, reducing response times by 30% and improving application scalability for future system expansions.- Collaborated closely with the quality assurance team on Failure Mode and Effects Analysis (FMEA) by integrating custom software tools for real-time defect monitoring, improving production line efficiency by 25%.- Introduced predictive analytics using TensorFlow, successfully reducing product failure rates by 15% through early detection of potential defects based on historical data.

- Developed a document management system using Flask and Angular, streamlining the alignment of technical documentation with legacy systems and automating the creation of control plans and pFMEA documents, leading to a 35% reduction in document approval time.- Deployed serverless solutions using AWS Lambda for automating large-scale data processing related to manufacturing quality control, reducing infrastructure costs by 25% and improving processing times by 20%.- Integrated REST APIs to ensure seamless synchronization between legacy manufacturing systems and modern documentation platforms, improving data integrity and reducing manual updates by 40%.- Enhanced internal application security by implementing OAuth 2.0 for user authentication and authorization, ensuring compliance with FDA and ISO regulatory standards.

- Built and deployed a real-time data visualization platform using Node.js, React.js, and MongoDB to track manufacturing non-conformances, leading to a 15% reduction in product defects and a 20% improvement in response times to production issues.- Automated the generation and tracking of non-conformance reports (NCRs) using Python and integrated them with internal databases, reducing manual reporting by 50% and improving data accuracy.- Deployed the platform on Google Cloud Platform (GCP) using Kubernetes and Docker for containerization, ensuring high availability and scalable infrastructure to meet fluctuating production demands.- Conducted performance testing using Cypress and JMeter, ensuring the system could handle large data volumes and high traffic without compromising real-time performance.

- Developed and maintained a web-based system using Django and React.js for managing first-piece and final inspections, automating metrological study reports and improving reporting accuracy by 25%.- Integrated automated Gage R&R studies using Python and Selenium, reducing manual quality checks by 20% and ensuring compliance with quality control standards.- Deployed cloud-based solutions on Azure, enabling real-time data sharing between multiple facilities, improving cross-team collaboration and streamlining the inspection process across two locations.

- Developed an internal property management system using Java Spring for the back-end and React.js for the front-end to manage renovation activities and maintenance tasks across multiple apartment properties. The system automated scheduling, work orders, and communication with tenants, reducing tenant response times by 40%.- Integrated real-time notifications using Firebase to alert tenants and maintenance teams of upcoming or completed tasks, significantly improving communication and reducing delays in response to maintenance requests.- Optimized database queries using MySQL, reducing the time to retrieve tenant and property data by 30%. This helped reduce system bottlenecks during peak hours of usage by maintenance staff.

- Developed web-based applications using React.js and Flask to monitor non-conforming product reports (NCRs) and gauge design data across multiple production cells. These applications allowed engineers to track KPIs in real-time, reducing the time needed to identify and resolve non-conformances by 20%.- Led efforts to develop a custom inventory management system for gauge design and calibration, allowing real-time monitoring of gauge usage and automated alerts for calibration schedules. This system was integrated with Zimmer Biomet's manufacturing process to ensure compliance with FDA regulations.- Implemented CI/CD pipelines using Jenkins and Docker to automate the testing and deployment process, reducing system downtime during updates and improving the overall reliability of the software used across production facilities.

- Developed a web application for Design History File (DHF) management using Django and Angular to automate the process of revising DFMEA and PFMEA documents. This system improved collaboration between design and quality control teams, reducing documentation errors by 15% and helping Zimmer streamline FDA audit preparation.- Designed REST APIs that allowed different departments to share DFMEA and PFMEA data across applications, ensuring that updates were automatically reflected across all relevant systems and reducing duplication of efforts.

- Developed a suite of internal tools using Python and Selenium to automate quality assurance processes for extruded and molded medical devices. These tools reduced the time required for non-conformance analysis by 25%, helping the company meet tighter production deadlines.- Created dashboards using React.js for real-time QA monitoring, allowing the quality team to respond more quickly to issues and maintain compliance with ISO 13485 and FDA regulations.- Worked closely with the product development team to validate manufacturing processes and implement corrective actions based on the results of failure analysis.

- Developed and maintained automated test frameworks using Python for CT lab processes, reducing manual testing time by 50%.- Built custom applications using Django and React.js to track and report test results in real-time.- Implemented machine learning algorithms for anomaly detection in CT data, improving the accuracy of failure predictions.

- Developed a laboratory management system using Python and PostgreSQL to manage testing data, significantly reducing processing times for raw material testing.- Automated the data collection process using Python scripts, reducing human error and improving efficiency.- Implemented RESTful APIs to integrate lab data with company-wide systems for enhanced transparency and reporting.