The rapid advancements in AI, particularly with the emergence of large language models (LLMs) and their diverse applications, have attracted huge global interest and raised significant concerns on responsible AI and AI safety. While LLMs are impressive examples of AI models, it is the compound AI systems, which integrate these models with other key components for functionality and quality/risk control, that are ultimately deployed and have real-world impact. These AI systems, especially autonomous LLM agents and those involving multi-agent interacting, require system-level engineering to ensure responsible AI and AI safety. On the other hand, data is the lifeblood of AI systems, cross-cutting different components in AI systems. There are various challenges associated with the data collected, used, and generated by AI systems, as well as their engineering processes. In this talk, I will introduce a responsible AI engineering approach to address system-level responsible AI challenges. This includes engineering/governance methods, practices, tools, and platforms to ensure responsible AI and AI safety. Specially, I will focus on how the responsible AI engineering approach tackles data challenges within the context of responsible AI.