Developed REST API infrastructure using AWS API Gateway and Lambda functions to automate client onboarding and project quota allocation, supporting scalable client management operations

Wrote testing protocols with pytest framework, achieving 90% code coverage through integration and unit testing to validate API functionality, debugging and performance monitoring

Developed CI/CD pipelines using Spinnaker and Jenkins with Docker containerization, automating lambda deployments and optimizing API concurrency for uptime and low latency

Engineered end-to-end custom API endpoints with integrated business logic and documented customer-facing solutions for knowledge transfer

Developed Python visualization and analysis scripts to process L-rail embedded sensor data, enabling comprehensive railroad infrastructure monitoring and performance assessment for research initiatives

Built automated web scrapers and crawlers with multiprocessing Selenium pipelines, implementing bash scripts for routine data extraction and processing to streamline railroad data collection workflows

Developed a machine learning classification model using Python to detect review bombing patterns by analyzing metadata and user reviews, successfully identifying fraudulent rating manipulations across entertainment platforms

Engineered ETL pipelines to process large-scale datasets containing 45,000 movies, 26 million ratings, and 27,000 users, Incorporated knowledge graphs and critic reviews as control variables, leading to a 10% improvement in F1 score compared to the baseline model

Automated the ETL process of student performance data gathered using RESTful API reducing processing time by about 50%

Designed an interactive dashboard using Matplotlib, ggplot, Altair, and Plotly to visualize key performance indicators, resulting in an 80% increase in user clicks and engagement

Developed a predictive maintenance model using Random Forest to anticipate sensor failures, contributing to a 20% reduction in downtime and improved system reliability

Collaboratively developed an Early Warning System leveraging machine learning techniques to detect fraudulent transactions, reducing false positives by 30%

Applied Minority Over-sampling (SMOTE) to balance class distribution and used Self-Organizing Maps (SOM) for unsupervised clustering, building an anomaly detection pipeline with Python and scikit-learn that improved model robustness and detection accuracy

Programmed Web Data Scrapers to extract data from official public sources, increasing the volume of collected data by 50%