Can you describe a complex technical problem you faced where the requirements were unclear or kept changing? Walk me through how you approached it from understanding the problem to the solution.

Situation: I was tasked with building a real‑time fraud detection system for a payment platform. The business requirements were evolving daily as new fraud patterns emerged. Task: Deliver a scalable pipeline that could ingest transaction data, apply ML models, and alert fraud analysts. Action: I first clarified the core objectives with stakeholders, identified the minimal viable set of features, and documented assumptions. I then designed a modular architecture using Kafka for ingestion, Spark Structured Streaming for processing, and a model registry for versioning. I set up a CI/CD pipeline to deploy model updates automatically. I also implemented a monitoring dashboard to track latency and accuracy. Result: The system was deployed within 8 weeks, reduced false positives by 30%, and allowed analysts to investigate suspicious transactions in real time. It also provided a flexible framework that accommodated new requirements with minimal code changes. The architecture also allowed for easy integration of new data sources and model updates, ensuring the system remained adaptable as fraud tactics evolved.