People can encounter and use AI in their work in many different ways. Here are three common examples:
You can work as a professional who has a good understanding of AI methods and the ability to ideate realistic solutions that involve an AI component. The ability to combine an understanding of AI with knowledge from other fields can lead to valuable results, even if you’re not writing the code yourself.Those who are more interested in the infrastructure behind what makes AI systems tick can work, for instance, as Data Engineers. A Data Engineer makes sure that data flows where it is needed and is of the right type and quality for specific AI solutions. It’s crucial with real-life, large-scale, production-ready AI systems to get the supporting infrastructure right so as to avoid the so called “garbage in, garbage out” problem.Those who have a deep understanding of AI algorithms can apply these methods to real-life problems with job titles such as AI Developer, AI Engineer, or Data Scientist. To excel in this area, you need to have a proven track record in applying AI to different types of problems. In addition to the technical skills, you need to know what’s possible, what’s not possible, and – often most importantly – when you should apply AI in the first place, and when not.The world seems to be full of people who claim to be AI evangelists but what modern companies are looking for is people who can make things happen and who actually understand what AI is and what it isn’t capable of
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