Drove the iteration of AI/ML platforms in the TikTok Content Ecosystem and AI Platform team for ML model evaluation and content moderation.

• Enabled ML model back-scan task management for easy control and monitoring.

• Built a filter rule editor for TikTok content moderation, allowing non-technical partners to create complex logical/comparison/nested queries across ML signals and video metadata.

• Released a batch labeling panel that displays video labeling options with their model signals side-by-side for efficient annotation.

Led and owned the TikTok Help Center for the Ecosystem Platform team, ensuring cross-regional data consistency and security compliance.

• Launched the user-friendly external FAQ website - new TikTok Help Center, built with React, GraphQL, and a MongoDB-backed CMS with cross-region synced Redis caching; expanded I18N coverage from 24 to over 30 countries through canary deployment via CI/CD to ensure zero-downtime rollout.

• Eliminated recurring P0 incidents caused by cross-team code coupling; established service boundaries, refactored shared dependencies, and conducted reviews focused on error handling and maintainability, reducing review cycles.

Engineered scalable monetization(m10n) backend services on GCP in the YT Brand Connect Eng team, expanding support for international customers.

• Delivered creator-facing web applications with payment internationalization(i18n), backed by an automated data pipeline to integrate YT Data Warehouse and streamline Adsense payment reconciliation automation for better accuracy and user experience.

• Enabled automated push notifications using Pub/Sub for event-driven processes, along with E2E testing to enhance real-time updates.

• Developed next-gen Marketplace and Creator Studio features using industry best practices, contributing to enhanced project lifecycle management and a sustainable platform architecture for over 360 clients and 1k+ client campaigns.

Worked in the Analytics Platform team serving the Amazon Care App on Java and AWS Lambda architecture, collaborated with senior teammates to develop and deliver resilient data lakes and warehouses, enhancing data accessibility and decision-making capabilities.

• Created an AWS CloudFormation (IaC) stack for an ETL pipeline from AWS DynamoDB to S3 as a prod-level data lake storage - practical solutions to run analytics on large-scale distributed systems with exposure to incredible volume and velocity.

• Streamlined automated integration tests in Hydra/Docker for accelerated validation and boosted productivity by 25%.

• Leveraged ECS, Lambda, and CloudWatch for logging and auto-scaling, supporting millions of daily transactions after deployment.