The growing diversity and heterogeneity in (pre-)exascale high-performance computing poses significant challenges to software developers, including performance portability and code maintainability. To tackle these issues, developers have adopted various strategies, such as code duplication (typically abstracted internally by some application-specific interfaces), (semi-)automatic code translation, directive-based portability models, and high-level abstraction frameworks. For our SISSO++ code, which is an AI application not readily amenable to the well-established (and portable) AI frameworks like, e.g., Tensorflow, we opted for the MPI+X paradigm which is well established in HPC, specifically using MPI+OpenMP+Kokkos. The usage of the Kokkos abstraction framework enhances both the code performance and portability, and it also helps reduce code-maintenance burdens. The Kokkos framework is also expected to pave the way for adopting the parallel abstract concepts in future C++ language standards. Due to the generality of the Kokkos framework, and already proven for SISSO++ by a seamless transition over two generations of Nvidia GPUs, we anticipate that our SISSO++ code will easily adapt also to future HPC architectures. Our porting strategy outlined here can serve as an example for other nonneural network based AI code development efforts.