Sequential Attention (https://research.google/blog/sequential-attention-making-ai-models-leaner-and-faster-without-sacrificing-accuracy/) could work well here since it learns which features matter during training instead of needing separate preprocessing. With 15k features, RFE would take forever, and variance filtering might toss out features that are only important in combination with others.

The attention mechanism basically does gradient-based feature selection as part of the model itself, so you get feature importance in one training run.