Especially in the edge / embedded space, MLEng will imply more than just doing ops.

Stuff to do could include: - Getting a network architecture to run. - Applying optimization depending on target arch (pruning, quantisation, custom cuda kernels, etc). - Integrating models (rule of thumb: a product is 95% ordinary code, 5% is ML related). - Constructing benchmarks, monitoring