Telecom Industry is facing significant operational challenges with an aging billing data pipeline that is prone to errors, requires frequent manual rework, and lacks robust monitoring. This inefficiency directly impacts service reliability and consumed valuable engineering time. I re-architected and rebuilt the entire billing pipeline from the ground up using Python on the Google AI Studio (Vertex AI). A key part of this process was standardizing the data flow for all subscription, usage, and disconnect events and engineering the jobs to be idempotent, which fundamentally eliminated data inconsistencies and the need for manual reruns. To enhance system intelligence and reliability, I designed and implemented an automated anomaly detection system to identify issues like missing or duplicate charges and stuck jobs. This system was integrated with PagerDuty to provide real-time alerts to the on-call team, introducing a proactive AIOps capability. I further strengthened the pipeline's resilience by adding API validation, automated retries for transient failures, and safe replay mechanisms.