* Introduced gRPC for communication between Node.js and Python microservices, cutting pipeline creation time by 30 to 40% compared to the previous REST-based approach by eliminating serialization overhead and enabling strongly typed contracts across service boundaries.
* Shipped real-time pipeline status updates to the UI using WebSockets backed by Redis Pub/Sub, so users see start, failure, and completion events as they happen instead of polling or refreshing.
* Configured KEDA to autoscale Node.js and Python consumers based on live RabbitMQ queue depth, scaling pod count dynamically as queue depth crossed configured thresholds, keeping CPU and memory usage within healthy bounds during peak traffic and reducing average response time by 200ms compared to the previous fixed consumer pool.