Modern data engineering is shifting from traditional, rule-based ETL processes toward AI-native architectures capable of processing unstructured data. This evolution is facilitated by integrating the NVIDIA Triton Inference Server (TIS) into the "Transform" and "Load" stages of data pipelines to enable real-time enrichment and semantic validation. TIS serves as a high-performance inference engine that supports multiple frameworks, including TensorFlow, PyTorch, TensorRT, and ONNX Runtime, allowing for seamless deployment across both GPU and CPU environments. Key technical capabilities include dynamic batching, which aggregates individual inference requests to maximize GPU throughput and reduce per-record latency, and concurrent model execution, which supports complex ensembles where outputs from one model serve as inputs for another. Practical implementations include using NVIDIA NVTabular within Triton ensembles to ensure feature engineering consistency between training and inference, as well as utilizing NLP models for semantic data validation and automated content moderation. By supporting asynchronous execution via gRPC and HTTP protocols, TIS allows orchestrators like Apache Airflow or Kubeflow to interact with models non-blockingly, preventing bottlenecks in high-volume pipelines processing millions of records.