AI Engineer Bootcamp 2026: LLMs, RAG, AI Agents & Vector DBs

About this course

Become a job-ready AI Engineer and master the skills companies expect in 2026: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and vector databases. You will follow an AI-engineer roadmap from foundations to deployment, so you can design, build, and ship production-grade AI features instead of just calling APIs.

This course is built for developers who want to transition into AI engineering roles and need a single, practical path that covers LLM concepts, RAG pipelines, agents, evaluation, and deployment. Every module ties skills directly to what AI engineer roadmaps and job descriptions list as must-have capabilities in 2026.

What you'll be able to do as an AI Engineer

Understand and explain the AI engineer skill stack: LLMs, RAG, AI agents, evaluation, and deployment.

Build LLM-powered applications with modern APIs and frameworks, using patterns you can discuss in interviews.

Design and implement RAG pipelines with embeddings, vector databases, and retrieval strategies that ground models in real data.

Create AI agents that use tools, plan multi-step workflows, and interact with external APIs like a real product feature.

Evaluate and debug AI systems using practical metrics - accuracy, hallucinations, latency, reliability - that matter in production.

Deploy AI services and integrate them into web backends or existing products so your work looks production-ready on a CV and portfolio.

Projects you'll add to your portfolio

An LLM-powered Q&A assistant grounded in your own documents using RAG and a vector database.

An AI agent that calls external tools and APIs to complete multi-step tasks, showcasing planning and tool-use.

A production-style AI microservice that exposes LLM and RAG functionality over an API, ready to plug into a real app.

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