Q: Data Scientist → AI Engineer

Your head start: conceptual depth. You understand how models behave, what a probability distribution is, why outputs vary — which means concepts like temperature, embeddings, and evals click faster for you than for anyone else. Evals in particular reward your experimental mindset.

Your gap: production engineering. If your Python lives in notebooks, the distance is moving from investigating to shipping: version control, APIs, deployment, code other people run. Deployed projects close that gap faster than anything else, because they force the engineering habits.