Secure LLM-Oriented Data Engineering Pipelines for Scalable AI Workflows in Multi-Cloud Environments

Advances in LLMs have altered AI analytics, yet they are not yet easily brought into scalable, secure and efficient engineering systems—especially in settings with various cloud platforms. The paper offers a new design for LLM-driven data engineering processes that resolves major issues in collecting, processing and publishing data on the cloud. A proposed framework includes federated orchestration, encryption of all data and access control policies without trust, to achieve proper data privacy and adherence to rules, as well as ensure fast realtime processing of both unstructured and semi-structured data. In addition, we introduce a new scheduler and metadata manager to automatically optimize things like building prompts, training and inferencing using machine learning models. Extensive study using common benchmark sets on AWS, Azure and GCP demonstrates that there are 35% fewer pipeline problems, cost is reduced by 28% and the system performs more effectively and is more resilient. This research outlines how secure and scalable use of AI systems powered by LLMs can be spread across various multi-cloud platforms.