What's the most common mistake people make trying to become an AI engineer in 2026? Spending too much time on theory. They sign up for courses on calculus, linear algebra, probability, and traditional machine learning theory, then spend months working through mathematical proofs before writing a single line of application code.
The fundamentals are useful — but no one is going to hire you to write loss functions from scratch or prove why an optimization converges. That's research territory, a completely different role.
Your job as an AI engineer is to take the models that already exist and build products people actually use.
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