Can you explain your recent project?
💡 Model Answer
My recent project was an e‑commerce recommendation engine for a retail client. The goal was to increase average order value by providing personalized product suggestions. I designed a real‑time pipeline that ingested clickstream data from Kafka, processed it with Apache Flink, and stored aggregated user profiles in Redis. The recommendation logic was implemented in Python using scikit‑learn’s collaborative filtering model, which was served via a FastAPI endpoint. I also built a dashboard in Grafana to monitor key metrics. The project reduced recommendation latency from 5 seconds to 200 milliseconds and boosted conversion rates by 12%. I coordinated with data scientists, DevOps, and QA to ensure smooth deployment and continuous improvement.
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