A statistical model. I'm personally partial to using the ETS model (error, trend, seasonality), but a SARIMAX is also another good one. The 'issue' with a stats model will be that you need to do some hand tuning and thus need to understand how the model works (and ETS is a fairly simple one to comprehend).

You don't have enough data to use AI, you're likely just going to overfit the series. In fact, time series are usually fairly short which led to the whole forecasting community to erroneously think ML could never be used for forecasting (see the M4 competition). Statistical is the way to go in your case.

If you absolutely want ML, use a simple random forest library.

It looks a bit crammed, so I'd ditch the summary. I never read those anyway and if I can't tell at a glance what you've worked on, something is wrong. If you want to keep it, I would emphasize which sub branch of AI you're interested and/or specialized in.

I would emphasize that full-time industry experience: that'll tell me I won't need to show you how to work in a team and that mnist isn't the usual dataset quality you should expect. Do point out it's full-time. You can deduce it from the dates but I typically look at CVs for max 1 min for initial triage.

Otherwise it looks pretty great!