What you'll learn:
- Master the architecture and workflow of a RAG system for processing PDFs and multimodal data.
- Master the Fundamentals of AI, Machine Learning and Deep Learning (Basics)
- Master LangChain tools, frameworks, and workflows, including embedding techniques and retrievers.
- Fine-tuning models with OpenAI, LoRA, and other techniques to customize AI responses.
- Develop AI-driven applications with advanced RAG techniques, multimodal search, and AI agents for real-world use cases.
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.
Additional mini-projects that demonstrate prompt engineering, evaluation workflows, and AI-powered automation.
You can reference these projects in interviews, GitHub, and LinkedIn to prove you can design and ship full AI workflows, not just toy demos.
Who this course is for
Software Engineers and Backend or Full-Stack Developers targeting AI Engineer roles.
Data Scientists and ML Engineers who want to move into LLM and agent-centric work.
Career-switchers and motivated beginners who want a guided AI engineer roadmap instead of random tutorials.
Tech professionals who want to add AI Engineer skills to their current role and stand out in 2026.
Requirements
Comfortable with basic Python (loops, functions, packages); some API experience is helpful but not required.
No prior deep learning experience necessary; we cover the essentials as they relate to AI engineer work.
A computer with internet access to run notebooks, call APIs, and connect to vector databases.
If your goal is to apply for AI Engineer roles, talk confidently about RAG, agents, and vector databases, and ship projects that match modern roadmaps, this course is designed to get you there.
