In your resume, you mention being a data engineer and AI engineer. How do you apply those skills in your current project?

I combine data engineering and AI by building end‑to‑end pipelines that feed machine learning models. First, I design scalable ingestion using Kafka and Spark, then perform feature engineering in Spark or Flink, storing features in a feature store (e.g., Feast). I train models in a Jupyter environment or SageMaker, packaging them as Docker containers. These containers are deployed to a Kubernetes cluster or Lambda for real‑time inference. I also set up monitoring dashboards with Prometheus and Grafana to track data drift, model accuracy, and latency. In my current project, I automated the entire flow: data ingestion from IoT devices, feature extraction, model training nightly, and real‑time scoring for anomaly detection, reducing manual intervention by 70%.