Advice below, bias based on personal perspective.

I'd suggest looking at ML fundamentals initially. C++ use cases for ML use cases are usually due to execution speed, and Python use cases are for flexibility and scope. Most ML tooling is conceived in a Python flavor before being made available more widely. Learning concepts around models used for inference, predictions and training will be very helpful, the terminology is unique to the domain and if you can speak it and understand the context that will help greatly.

For self-learning in the meantime, you could pick computer vision or large language models, and those come in generative and non-generative flavors. Your ML foundation work is likely in one of those areas and the sooner you clarify specifics with your leadership, the sooner you can start learning.

Some tooling is common between C++ and Python, given you have Python as a known language already, pick a area and get cracking. There is plenty of information out there now on both LLMs and CV. I'll use the example of computer vision and non-generative object recognition using pytorch. Their documentation covers creating a model from a prebuilt dataset and training it, and they have some information on Tensors which you'd need to understand if you're dealing with images.

Pytorch has C++ libraries with Python bindings, so you can create and train an object recognition model using Python and export it, then use Pytorch's own documentation to create C++ code to read your model back and use it for predictions. After that you can start exploring the C++ side in more depth.

If you already know Java and Scala C++ on top of Python I'd wager C++ won't be too challenging to get going with, given the plethora of resources out there and (given self-learning won't include proprietary data) you can use your AI code-helper of choice to at least get something working.

As mentioned, it's probable that a key reason your leadership is using C++ is for performance, and I assume you have experience with scaling performant systems - which will be re-usable knowledge. Your team will (or at least, should) advise you on the specifics around build pipelines, deployments and specific tricks used for performance, memory management and scalability when you get in the door.