• Architected microservices backend supporting 50K+ active users across 5 civic technology projects using Python, Django, PostgreSQL, and RESTful APIs, enabling scalable service delivery for LA residents

• Accelerated code review time from 2 hours to 45 minutes per pull request by implementing modular architecture patterns and robust unit testing suite using PyTest

• Integrated LA Metro's real-time transit API with error handling and caching layer to manage inconsistent data formats and API downtime, contributing to 23% ridership increase among 30K+ users and $1.2M estimated emission reduction value

• Automated administrative processes saving 60+ hours monthly across 15-person team using AWS, enabling focus on core development work

• Implemented comprehensive testing strategy increasing code coverage from 40% to 97.6%, eliminating production bugs and minimizing hotfix deployment frequency from weekly to monthly

• Diagnosed and resolved critical Docker infrastructure failure affecting 15-person team, restoring uptime within 48 hours and preventing estimated $15K in lost productivity

• Contributed to 100+ GitHub commits, 5+ code pushes per week, and 5+ pull request reviews weekly across multiple Agile sprints

• Developed real-time content filtering engine using Python and SQL processing 50M+ daily requests, protecting 400M+ users from malicious content and preventing estimated $50M+ in potential security breaches annually

• Designed automated content classification system reducing manual review time from 72 to 4 hours, processing 100K+ websites monthly and cutting operational costs by $180K annually

• Optimized ML pipeline reducing false positives by 65% and cutting manual moderation costs by $2.4M+ annually across global operations

• Authored training materials that shortened onboarding time for 7 new analysts, compressing ramp-up from 4 weeks to 2.5 weeks

• Collected and analyzed website data (text and images), ensuring full-spectrum coverage for content filtering