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This course examines the integration of contemporary generative-AI techniques into systems engineering practice, emphasizing applications in aerospace, defense, and transportation. Students analyze the theoretical underpinnings of large language models and related transformer architectures; assess methods for requirements generation, design exploration, Retrieval-Augmented Generation (RAG) pipelines, and agent-based workflows; and evaluate governance, safety, and ethical considerations across the systems engineering lifecycle. Virtual laboratory sessions provide hands-on experience with frontier cloud models and locally hosted open-source counterparts, enabling participants to prototype requirement refactoring, code and model synthesis, simulation test generation, and multi-agent orchestration while maintaining rigorous verification, validation, and human-oversight mechanisms essential to high-consequence engineered systems.