Xiaobo Guo

• Automated a large-scale cross-cloud data migration from Alibaba Cloud to AWS, successfully transferring over 2TB of video data with zero data loss, optimizing storage costs by 20%. • Designed and implemented end-to-end CI/CD pipelines on Github Action, reducing deployment failures by 25%. • Implemented and maintained Terraform code, resulting in a 30% reduction in deployment time and a 20% decrease in system errors. • Automated backend deployments for UAT and Prod environments using technologies such as ECS, NAT Gateway, Load Balancers and Security Groups, resulting in a 50% improvement in deployment speed. • Implemented frontend application deployment, integrating AWS Route 53 for DNS management and AWS Certificate Manager (ACM) for SSL certification, ensuring a secure and accessible user interface. • Established monitoring and logging frameworks with Prometheus, Grafana, and CloudWatch, reducing system downtime by 15%. • Collaborated in sprint planning, stand-ups, and retrospectives to foster cross-functional teamwork and agile delivery.

Objective: Working as a DevOps engineer in a team of 6 Developers, 3 DevOps, 2 Business Analyst and developing a social application connecting pet lovers. The goal is to create a comprehensive web portal(Nodejs & Reactjs) for Australian pet lovers to communicate experiences, share ideas and make friends. It integrated OpenAI’s Chatgpt to create a Q&A feature for users, which can answer a wide range of personalized questions. Achievement: • Established multiple CI/CD pipelines on Jenkins, reducing manual effort by 20% and decreasing deployment time by 35%. • Implemented infrastructure as code using Terraform, optimizing the infrastructure provisioning process and reducing human error by 20%. • Automated backend deployment for production environments using AWS ECS and Fargate, resulting in a 50% improvement in deployment speed. • Improved code quality by implementing Synk and SonarQube and monitored application performance with Grafana dashboards.

Objective: Working as a Test engineer in a team of 6 members to implement a validated artificial neural network into a software suite for user-friendly human joint motion measurement from IMU data. Achievement: • Analyzed Chinese and Australian markets, purchased and analyzed sensors with similar functions. Presented findings to cross-functional teams, informing product development decisions. • Identified and implemented cost-effective sensor alternatives, resulting in a 90% reduction in expenses while maintaining comparable performance through rigorous testing. • Utilized SDK to extract sensor data from hardware and integrate it into software applications. Streamlined the data transfer process, improving efficiency. • Implemented data filtering and cleansing techniques to eliminate sensor drift noise. Developed automated scripts to detect and remove outliers, improving accuracy by 10%. • Collected real-time data streams and calculated joint angles from sensor data. Implemented real-time data log and stream visualization, enhancing monitoring capabilities and facilitating immediate adjustments. • Collaborated with software team to seamlessly integrate new sensors with the software suite. Contributed to a 15% reduction in development time through effective teamwork and coordination.