From LLMs & RAG to safety & governance — the exact courses you need to break into AI engineering in 2026.
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NVIDIA Deep Learning Institute
LLMs & RAG
The most in-demand skill in AI Engineer job descriptions. Learn to build and deploy RAG agents with LLMs using LangChain, FAISS, and NVIDIA AI endpoints. Covers document chunking, embedding pipelines, vector stores, and production scaling strategies.
Use AI to build and control agents. Covers the technical implementation of MCP (Model Context Protocol) servers and clients — from basic message passing to production deployment strategies, sampling, notifications, and transport mechanisms.
Deploy and test AI products at scale. Deep-dives into the Evaluation Harness — a three-stage pipeline (Inputs → Execution → Actions) that stays consistent as your eval practice matures. Learn LLM-as-a-Judge, CI/CD gates, and automated monitoring.
Build responsible AI systems that comply with governance. A holistic course by Dan Hendrycks (Director, Center for AI Safety) covering AI risk, alignment, robustness, fairness, and institutional design. Read free online, audiobook on Spotify, or order in print.