AI Engineering Foundations

What is AI Engineering? AI Engineering is a new and distinct discipline focused on integrating pre-trained models into applications — building RAG systems, agents, and LLM-powered features that solve real business problems . This is fundamentally different from traditional Machine Learning Engineering: ML Engineers train and optimize models AI Engineers use pre-trained models to build production applications The AI Engineer Role in 2026 Based on analysis of thousands of job descriptions, three types of roles hide under the "AI Engineer" title : Role Type Percentage Focus AI-First ~70% Building RAG systems, agents, LLM features AI-Support ~25% Building platforms, infrastructure, tooling ML ~5% Traditional ML rebranded What You Will Learn By the end of this 5-day course, you will be able to: Understand the AI Engineer role and how it differs from ML Engineering Work with LLM APIs and design effective prompts Build Retrieval-Augmented Generation (RAG) systems Create AI agents with tool use and function calling Evaluate and monitor AI systems in production Prepare for a career as an AI Engineer Prerequisites Basic Python programming knowledge Understanding of APIs and REST concepts Familiarity with command line basics No prior AI/ML experience required The AI Engineer vs ML Engineer Aspect AI Engineer ML Engineer Primary Focus System integration, applications Model development, training Core Work RAG, agents, orchestration Training, optimization, experimentation Tools LangChain, vector databases, LLM APIs PyTorch, TensorFlow, scikit-learn Key Question "How do I build this product?" "How do I make this model better?" Aspect AI Engineer ML Engineer Data Interaction Application data, documents Training datasets, feature engineering