The title "data scientist" is quietly disappearing from job postings, internal org charts, and LinkedIn headlines. In its place, roles like "AI engineer," "applied AI engineer," and "machine learning engineer" are becoming the norm. This Data Scientist vs AI Engineer shift raises an important question for practitioners and leaders alike: what actually changes when a data scientist becomes an AI engineer, and what stays the same? More importantly, what skills matter if you want to make this transition intentionally rather than by accident?