Driving technical strategy for policy compute infrastructure serving 4,000+ enterprise customers; defining architecture for next-generation distributed scheduling and caching systems

Re-engineered caching from per-process in-memory stores to a centralized Redis cluster with pipelining, multi-exec, consistent-hash sharding, and read replicas — consolidating ~20x redundant caches into a single shared layer

Led evaluation and migration from Redis to DragonflyDB, achieving ~4x throughput improvement while enabling horizontal scaling without application-layer changes

Designed and filed patent for a Natural Language → KQL AI pipeline with custom validators, model chaining, and self-improving feedback loops

Optimized core policy compute engine (Rails, PostgreSQL, Redis, Kafka), reducing average query latency by 35% through index tuning and connection pool optimization

Diagnosed and resolved production Kafka consumer lag and Memcached connection pooling issues behind HAProxy, cutting P99 response times and eliminating recurring outages

Implemented Sidekiq-based job processing for multi-tenant workload management; led schema migrations improving performance on 100M+ row datasets

Innovated with Flutter applications to enable real-time health metric sharing in sports activities, achieving a 22% reduction in battery consumption for smartwatches with BLE, complemented by a robust analytics dashboard designed with React

Implemented automated testing and test-driven development practices for storing user's health metrics and workout information using MongoDB and SQL server database within a Django-based web application