At Thrivify AI, I built and owned the AI feature pipeline for a production AWS Bedrock integration, including prompt architecture and output validation. This reduced the hallucination rate by an estimated 60%.
I also designed backend services spanning authentication, REST APIs, asynchronous message queues, and Redis caching. After resolving N+1 and indexing issues across MySQL and MongoDB, I reduced query latency by an estimated 40%.
I shipped more than 10 full-stack features using React, TypeScript, and NestJS, cutting average cycle time from two weeks to under five days. I was promoted from intern after six months.
In my projects, I built an agent-testing harness for paraphrased task variants and repeated trials, and implemented MCP-native context compression approaches based on ACON and Squeeze. I also built a real-time voice AI system that detects whether a human or voicemail machine answered a live call, with sub-second response latency and decision logging for auditability.
