Generative AI Engineer Mastery: LLM, RAG & Agentic AI

Overview

Build production-ready generative AI applications using LLMs, RAG, LoRA/QLoRA, fine-tuning & AI Agents

What you'll learn:

Build production-ready Generative AI applications using Large Language Models (LLMs)
Run and customize open-source LLMs locally with Ollama and cloud GPUs
Work with frontier models like GPT, Claude, Gemini, and Grok
Design real LLM engineering pipelines, not just prompt-based apps
Understand tokens, transformers, context windows, and inference costs
Build AI-powered backends and APIs using Python
Create interactive AI web applications with Gradio
Implement LLM tool calling and build intelligent AI assistants
Build Retrieval-Augmented Generation (RAG) systems to reduce hallucinations
Design and evaluate RAG pipelines with embeddings and vector databases
Apply advanced RAG techniques like re-ranking and query expansion
Fine-tune models using LoRA and QLoRA for cost-efficient customization
Prepare datasets and monitor training with Weights & Biases
Evaluate and benchmark LLMs using real-world AI leaderboards
Build agentic AI systems and autonomous multi-agent workflows
Deploy serverless AI applications to the cloud
Create a strong AI Engineer / LLM Engineer portfolio

Master Generative AI and Large Language Models by building real, production-grade AI systems — not just demos.

This course is a complete, end-to-end AI Engineering program designed to transform you into a job-ready AI / LLM Engineer. Over an intensive, hands-on 8-week journey, you will design, build, fine-tune, and deploy real-world AI applications using the same tools, architectures, and techniques used by top AI teams today.

You will move far beyond prompt engineering and chatbots. Instead, you will learn how to engineer scalable, accurate, cost-efficient, and autonomous AI systems using modern Large Language Models (LLMs).

By the end of this course, you will have built multiple portfolio-ready projects covering:

Retrieval-Augmented Generation (RAG)
Fine-tuning with QLoRA
Open-source and frontier models
Autonomous and multi-agent AI systems
Production deployment with polished user interfaces

This course is framework-agnostic, practical, and engineering-focused, making it ideal for developers who want real skills — not hype.

What You’ll Learn

Build advanced Generative AI products using modern LLM architectures
Work hands-on with 25+ frontier and open-source AI models
Design and implement Retrieval-Augmented Generation (RAG) systems to eliminate hallucinations
Fine-tune both frontier and open-source models using QLoRA and LoRA
Build autonomous AI agents with tools, memory, and planning
Engineer multi-modal AI applications using text, images, and audio
Transition from inference to training and fine-tuning confidently
Deploy AI systems to production with robust backends and polished UIs
Develop real-world AI projects suitable for interviews and professional portfolios

Hands-On AI Projects You Will Create

Throughout the course, you will design and deliver 8 fully functional, real-world AI systems that reflect how modern AI products are built in industry:

Intelligent Marketing Brochure Generator
Build an AI solution that intelligently explores company websites, understands their content, and automatically produces polished, business-ready sales brochures.
Multi-Modal Customer Support Assistant
Create an airline customer service AI capable of understanding and responding through text, images, audio, a modern UI, and tool/function calling.
AI Meeting Summary & Action Tracker
Develop an AI application that transforms recorded meetings into structured summaries and clear action items using both open-source and frontier models.
AI-Driven Code Optimization System
Engineer an AI tool that translates Python programs into highly optimized C++ code, delivering performance improvements of up to 60,000×.
Enterprise Knowledge Assistant (RAG)
Design a Retrieval-Augmented Generation system that becomes a domain expert by intelligently answering questions from internal documents and shared drives.
Capstone – Frontier Model Application
Build a real-world application that predicts product prices from short descriptions using leading frontier LLMs.
Capstone – Open-Source Fine-Tuned Model
Fine-tune an open-source language model using QLoRA to match or outperform frontier models for a targeted prediction task.
Capstone – Autonomous Multi-Agent AI System
Create a fully autonomous, multi-agent AI solution that collaborates across models to identify valuable deals and proactively notify users of opportunities.