AI Engineering in 2026 is basically 10 AI/DS jobs wearing a hoodie. You’re expected to know: → Python → SQL → ML / Deep Learning → LLMs + Prompt Engineering → RAG → MLOps → Data Engineering → System Design → Cloud → Deployment …and somehow make all of it work together. This is why I think a lot of Data Scientists feel overwhelmed moving into AI. They look at the AI Engineer skill stack and think: “Do I seriously need to learn ALL of this?” No. But you do need to understand how the pieces connect. That’s the big shift: Traditional Data Science was largely: Data → Model → Prediction AI Engineering is increasingly: Data → Models → LLMs → Tools → Agents → APIs → Infrastructure → Product You’re not just building a model anymore. You’re building a system that does work. And this is actually good news for Data Scientists. You already have a huge part of the foundation: Python. SQL. Statistics. Machine Learning. Data. You don’t need to start over. You need to layer AI Engineering on top of what you already know. That’s why the best career move right now is: Data Scientist → AI-Powered Data Scientist → AI Engineer Not abandoning Data Science. Expanding it. Want help? 🚨 Want to learn how to build + ship AI and Data Science projects (that businesses actually want in 2026)? On September 23rd, I am hosting a free workshop to help you get started with AI + DS projects in Python (free). Register here (500 seats): learn.business-science.io/ai…