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Utilization of Digital Engineering (DE) significantly enhances the lifecycle activities from concept through disposal, reducing required resources and time. DE of Liquid Rocket Propulsion (LRP) Launchers focuses on the application and integration of models and simulations to improve the process of their development. But what if you go further and use DE, assisted by experts, to train an Artificial Intelligence (AI). Afterward, the trained AI will minimize the human factor influence on DE of LRP launchers, and design them automatically for various missions and applications taking into account reusability aspects. The authors of this paper applied model-based engineering (MBE) and developed the design system focused on the conceptual phase of launcher lifecycle. The comprehensive system integration of the complex hierarchical structure of the models with AI training in mind was considered in the paper. This system allows the launcher design based on the minimal number of requirements, like orbit and payload; determines the launcher\'s number of stages and their mass, thrust requirements and dimensions at the rocket level; and then performs a preliminary design of optimal LRE including turbopumps, taking into account lifetime as one of the criterions for the best configuration selection to maximize the reusability potential of the developed engine. The system is comprised of models of different scales and types, from 0D and 1D models at rocket and LRE cycle levels to a combination of 1D, 2D and 3D models at turbopump level. This structure of the system provides the capability to quantitatively evaluate and optimize a launcher and its subcomponents with a sufficient level of detail. The detailed structure of the engineering system, its levels, and integrated models are described in the paper. Also, the utilization of the developed system to train an AxSTREAM.AI is considered.