The data-driven AI systems(e.g., machine/deep learning) continue to achieve substantial strides in enabling cutting-edge intelligent applications. However, the development of current data-driven AI systems still lacks systematic quality assurance and engineering support in regard to the adoption of quality, security and reliability assurance standards, as well as the available mature toolchain support in an interpretable way. In this talk, I would provide a high-level overview of our team's efforts in quality and reliability assurance and engineering support for building trustworthy AI systems in practice, while highlighting a few concrete innovations that are already adopted in practice. Furthermore, I outline challenges, opportunities, vision and future opportunities towards laying down the foundations for better engineering safe, secure and reliable AI systems.