I refactored an internal reporting and billing system, focusing on boosting performance and efficiency. Results:◆ Achieved a substantial improvement in the performance of the reporting system, with an increase of 300%-400% and saving about 20 % cost of labor.◆ Enhanced the efficiency of the billing system by 300%.◆ Monitoring system saves cost of labor and saves manual looks for problem timeProgramming Languages and Technologies Used:◆ Data Refactoring for Parallel Processing: Breaking down data into finer dimensions for parallel processing.◆ ETL Pipeline with AWS Step Functions or Airflow: Based on User or condition dynamically adjust the job flow.◆ Job Monitoring and Alerting by using Grafana and Cloudwatch Dashboard: Pull job logs, sqs message number, metrics of EC2/Fargate, and whole ECS cluster resource monitor and send alert.◆ Cost Optimization through Scalability: Dynamically adjusted machine numbers based on data size for optimal cost efficiency.

◆ Familiarity with various AWS services, particularly in IoT and Machine Learning domains.◆ Experience in a project involving integration of cloud and edge devices for an AI-powered camera solution for PCB defect detection, utilizing NVIDIA Jetson TX2.◆ Responsibilities included integrating edge and cloud components, training AI models for image classification, connecting other sensors, and implementing data transmission using AWS IoT SDK and MQTT.◆ Involvement in building a cloud-based educational platform using AWS S3 bucket for hosting static websites, React for front-end development, and API Gateway for RESTful API calls to Lambda functions for web scraping and recommendation of cloud-related lectures. Also responsible for setting up Hands-on Lab environments.

◆ Collecting data from our product and using Python to sort out the data and make visualization.◆ Setting up computer environment in RD lab, connecting to our NAS ( Network Attach Storage) system.◆ Assisted in debugging errors in the program.