How I'd Learn AI Engineering From Zero in 2026 (1 hour masterclass)

Learn all AI engineering skills in one place with Data Camps hand on tracks: 1️⃣ Associate AI Engineer course for devs: https://datacamp.pxf.io/qWVJQj 2️⃣ Associate AI Engineer course for data scientists: https://datacamp.pxf.io/PzBXeX AI is moving insanely fast, and most people are still only using a tiny fraction of what these tools can actually do. In this video, I break down the realistic roadmap I would follow if I had to learn AI engineering from zero in 2026 — not just how to use ChatGPT, but how to build real AI systems, work with agents, understand modern coding tools, and ship production-ready software with AI in your workflow. We'll cover the foundations you need first, including TypeScript, Next.js, Expo, Python, APIs, databases, architecture, and the programming patterns that help you actually understand the code AI writes for you. Then we go deeper into the AI engineering side: harnesses, agents, planning, context windows, model choice, MCPs, skills, plugins, memory, RAG, vector databases, automation, Docker, CI/CD, Linear, and the legal/data considerations you cannot ignore when shipping real products. We'll cover: ✅ What an AI engineer actually does in 2026 ✅ The coding foundations I would learn first ✅ How to read, understand, and review AI-generated code ✅ The difference between harnesses, agents, models, MCPs, skills, and plugins ✅ How to control AI costs with better planning, model choice, caching, and context management ✅ How memory, RAG, embeddings, and vector databases make AI systems smarter ✅ How to run agents safely with sandboxing, permissions, and isolated environments ✅ The production skills most beginners skip: Docker, CI/CD, Linear, legal, data privacy, and feedback loops This is the roadmap I wish more people followed before calling themselves AI engineers — because the real skill is not just vibe coding an app, it's building systems that are useful, safe, scalable, and actually shippable. 🕣 TIMESTAMPS: 0:00 Intro 2:45 What is an AI Engineer? 5:07 Learning AI with DataCamp 6:56 What AI Engineers Actually Do 8:20 What to Learn First 10:57 How Apps Actually Work 13:26 Reading AI-Generated Code 15:28 Programming Patterns You Need 18:11 AI Harnesses Explained 21:41 Plan Mode and Better Prompts 23:37 Agents.md and Project Rules 24:34 Source Control and Git 25:47 Git Worktrees for Parallel Agents 28:13 Models, Costs, and Reasoning Levels 32:16 Context Windows Explained 36:00 AI Subscription Plans and Usage Limits 37:11 Reducing AI Costs with Caching 38:32 MCPs and Agent Capabilities 40:22 Skills, Plugins, and Safer Agent Upgrades 45:45 Building AI Features with SDKs 47:14 Memory, Hallucinations, and RAG 49:51 How RAG and Vector Databases Work 53:44 Safe Execution and Agent Permissions 57:00 Automations and Durable AI 1:00:00 Building Your Own AI Team 1:01:39 Docker for AI Engineers 1:02:52 CI/CD and GitHub Actions 1:04:11 Linear, Agile, and AI Workflows 1:07:04 Legal, Data Privacy, and Compliance 1:08:11 Build a Feedback Loop 1:09:25 Rapid-Fire AI Engineering Rules 1:10:41 Credentials Are Not the Flex Anymore 1:12:00 Final Advice ⸻ DISCLAIMER: This video is sponsored by DataCamp. All opinions are my own — I only share tools I personally use and believe will help the developer community. Copyright Disclaimer: This video is made for informational and educational purposes only. Copyright Disclaimer under Section 107 of the Copyright Act 1976 allows "fair use" for educational purposes. —————— 🎓 Learn to code with AI here: https://www.papareact.com/course 👥 Join our AI coding community for FREE: https://www.universityofcode.com Follow me on socials: 🔗 Instagram: https://links.papareact.com/instagram 🔗 LinkedIn: https://links.papareact.com/linkedin 🔗 Twitter/X: https://links.papareact.com/x 🔗 TikTok: https://links.papareact.com/tiktok 🔗 Threads: https://links.papareact.com/threads