Incoming Software Engineer Intern (DevOps), Developer Experience Team · 2026

During my internship at WhiteCube (Challengers), I worked on backend systems that power the company’s advertising and ranking platforms.

Improved the product validation pipeline by replacing Tesseract with Google Cloud Vision API, boosting OCR accuracy from 90% to 97% and cutting P99 latency by 20%.

Built real-time ranking anomaly detection with hourly baselines, Slack alerts, and automated dashboards, enabling quick identification of algorithm changes.

Expanded data collection through large-scale crawlers and created internal tools for ranking trend visualization and brand performance analysis.

Added pre-submission validation logic using OCR and regex, reducing order verification failures by 90% and increasing success rate to 95%.

During my internship at AWS (Big Data Team), I worked on cloud automation and educational projects that enhanced real-world data engineering training for students and professionals.

Built a serverless lab project for an AWS-hosted STEM program by integrating GitHub Webhooks with Lambda, Bedrock, and DynamoDB. The system automatically analyzed pull requests with LLM-powered summaries and sent issue reminders through SNS.

Designed and implemented a 4-hour Cloud Computing assessment for WorldSkills 2025 participants, developing a real-world ETL pipeline using Kinesis Data Streams, Glue, and Athena. Built automated grading scripts with AWS CLI and led a 90-minute training session on pipeline design and data processing, earning a 4.52/5 participant satisfaction score.

Developed a study assistant chatbot using GPT API, simplifying computer science concepts for students with foundational topics of data structures, algorithms design, OS, Networking, Storage, and Database Management, im- proving understanding and study efficiency.

Participated in the LG Aimers Machine Learning competition, focusing on defect detection in vehicle display manufac- turing data. Ranked in the top 200 out of 800 participants by developing a predictive model to identify and classify defects using sensor measurements and equipment settings, successfully completing the competition.

Developed a machine learning application to predict pet adoption likelihood based on various features such as pet size, breed, color, and other attributes. Utilized advanced data preprocessing techniques, feature engineering, and model evaluation. Implemented XGBoost model for high accuracy, achieving a test accuracy of 94%.