Data Scientist to AI Engineer Roadmap for 2026

Want to start freelancing? Let me help: https://go.datalumina.com/p7mxlp7 Want to learn real AI Engineering? Go here: https://go.datalumina.com/pjWWw26 🔗 GitHub Repository https://github.com/daveebbelaar/ai-cookbook/blob/main/roadmaps/ds-ml-to-ai-engineer-2026.md Writing AI prompts all day? 🎙Try Glaido, the #1 voice dictation app for developers: https://get.glaido.com/dave ⏱️ Timestamps 0:00 AI Shift Roadmap 2:11 Why AI Engineering 3:03 Your Hidden Advantage 5:49 Closing the Software Gap 8:59 Mastering LLM Basics 13:23 Building Production Backends 16:55 Retrieval Augmented Generation 19:54 Evals and Guardrails 22:36 Ship Real Projects 26:10 Making the Career Switch 📌 Description In this video I explain how to transition into AI engineering from a data science or machine learning background, using my own move from data science into GenAI work as the basis for a step-by-step roadmap. I cover the software engineering skills needed to move beyond notebooks, including structured Python projects, Git, testing, debugging, logging, and environment management. I also walk through the LLM and backend stack, including the OpenAI Python SDK, prompt engineering, agents, FastAPI, Pydantic, Docker, PostgreSQL, and MCP servers. The final part focuses on RAG, evals, observability, tracing, guardrails, and building end-to-end projects that can be deployed and used as portfolio pieces for AI engineering jobs or freelance work. 👋🏻 About Me Hi! I'm Dave, AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials that teach developers how to build production-ready AI systems that actually work in the real world. Beyond these tutorials, I also help people start successful freelancing careers. Check out the links above to learn more!