So You Wana Be an AI Engineer?
Ok, I think most people do not aspire to be an AI Engineer.
Being an AI Scientist is sexier & is always at the front & center of bosses, consistently serenaded with smooches for executing seemingly difficult, but yet training the latest SOTA neural network architecture using transfer learning. But who cares, the model works, the attention & prestige is nice.
AI Engineers though, are the unsung heros. They understand modelling, they understand software engineering, and heck, they know how to do almost anything! Database, web development, cloud engineering, testing, deployment, and certainly how to do transfer learning too. 
Jokes aside, it is important to note that a mature ML system may contain only 5% of ML code, highlighting the need for a diversified skillset.
The AI engineer will also need to be familiar with the processes of the ML life cycle, and what to do within and between each process.
Besides their full stack technical capabilities, good AI Engineers also possess certain critical attributes.
- An incredible intolerance for slobby work
- Great organizational abilities
- A demand for reproducible work with strong documentation
- A strong focus on the security and reliability and know how to balance both
Such a wide range of expertise is difficult to find, so a company who values AI Engineers will not devalue them in terms of compensation.
Still not convinced? Well, I would say that I have seen a number of AI Scientists starting to learn more engineering aspects of AI. Heck, even they secretly aspire to be an AI Engineer, though they still want to wear their fancy scientists' hats. 
Now that I'm done with the rambling, if you are ready to hop on the bandwagon, let's get started.