Chad L. Stendal

Built end-to-end AI video generation workflows using Next.js and Node.js, moving UI features into queue-based back-end pipelines and storage. Reduced median generation latency by 38% with Redis-backed job queues, parallel workers, and smarter retry logic for failed model calls. Evaluated and integrated multiple third-party image/video model APIs while balancing output quality, speed, reliability, and per-generation cost. Cut monthly AI inference spend by 27% via request caching, model routing, and generation presets without degrading output quality. Improved production reliability to 99.9% uptime with DataDog dashboards, structured logging, and alerting on generation failure rates. Partnered with the CTO on architecture and mentored 2 mid-level engineers through remote code reviews.

Developed responsive React and TypeScript features for a media editing product, collaborating with design and product across distributed time zones. Built Node.js services and PostgreSQL schemas supporting uploads, processing, and delivery for user media for 250K+ monthly active users. Shipped an AI-assisted editing feature using third-party model APIs, increasing weekly feature adoption by 22% in the first quarter. Containerized media processing with AWS workers and CI/CD pipelines, reducing deployment time from 45 minutes to 12 minutes. Diagnosed production issues across front end and back end and wrote notebooks and regression tests to shorten repeat incident resolution.

Built user-facing features for a social media publishing platform using React and Node.js in a fully remote team. Integrated third-party social network APIs, handled rate limiting, authentication flows, and webhook-driven updates for scheduled content delivery. Optimized slow PostgreSQL queries and added Redis caching, reducing average dashboard load time by 31% for high-volume accounts.