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Intermediate Artificial Intelligence 40 hours

AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents

Become an LLM Engineer in 8 weeks: Build and deploy 8 LLM apps, mastering Generative AI, RAG, QLoRA fine-tuning, and AI Agents.

10 Chapters
40 Lessons
1075 min total
Open Access

This is the definitive hands-on course for aspiring AI Engineers. Over 10 intensive chapters, you will build 8 real-world LLM applications — from AI brochure generators and multi-modal customer support agents to autonomo...

What you'll learn

  • Build Your First LLM Product — Exploring Top Models
  • Multi-Modal Chatbot — LLMs, Gradio UI, and Function Calling
  • Open-Source Generative AI — Automated Solutions with Hugging Face
  • The LLM Showdown — Evaluating Models for Code and Business Tasks
  • Mastering RAG — Build Knowledge Systems with Vector Embeddings
  • Advanced RAG and Agentic RAG Patterns

+ 4 more chapters below

Engr Mejba Ahmed

Engr. Mejba Ahmed

Course Instructor

AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents

About This Course

This is the definitive hands-on course for aspiring AI Engineers. Over 10 intensive chapters, you will build 8 real-world LLM applications — from AI brochure generators and multi-modal customer support agents to autonomous deal-finding multi-agent systems. You will master the four pillars of modern AI engineering: LLM Engineering (working with frontier and open-source models), RAG (Retrieval-Augmented Generation with vector databases), QLoRA (efficient fine-tuning on consumer hardware), and Agents (autonomous multi-agent architectures). Every lesson is project-driven with production-grade Python code you can immediately apply to your job or portfolio. By the end, you will have 8 deployed AI projects, deep expertise in LangChain, Hugging Face, vector databases, and fine-tuning — and the skills to command a premium salary as an AI Engineer.

Course Curriculum

10 chapters 40 lessons 1075 min

6 lessons available to preview

1 Setting Up Your AI Engineering Environment
Preview 18min
2 Frontier Models Deep Dive — GPT-4o, Claude, Gemini
Preview 22min
3 Open-Source Models — Llama 3, Mistral, and Ollama
20min
4 Project: Build an AI Brochure Generator
30min
1 Understanding Multi-Modal Models — Text, Image, Audio
Preview 20min
2 Building Interactive AI Apps with Gradio
22min
3 Function Calling — Giving LLMs the Power to Act
25min
4 Project: Build a Multi-Modal Customer Support Agent
35min
1 The Hugging Face Ecosystem — Transformers, Datasets, and Hub
22min
2 Text Generation, Summarization, and Translation Pipelines
24min
3 Code Generation with Open-Source Models
22min
4 Project: Build an Automated Meeting Minutes Generator
30min
1 LLM Evaluation Frameworks — MMLU, HumanEval, and Custom Benchmarks
22min
2 Comparing 10 Frontier Models on Real Business Tasks
25min
3 Comparing 10 Open-Source Models — Performance vs. Cost
24min
4 Project: Build an Automated LLM Evaluation Pipeline
30min
1 Vector Embeddings — How Computers Understand Meaning
Preview 22min
2 Vector Databases — Chroma, Pinecone, and FAISS
25min
3 Building Production RAG Pipelines — Chunking and Retrieval
30min
4 Project: Build an AI Knowledge Worker for Company Docs
35min
1 Advanced Chunking — Semantic, Hierarchical, and Contextual
24min
2 Hybrid Search — Combining Dense and Sparse Retrieval
26min
3 Agentic RAG — Self-Correcting Retrieval with LangGraph
28min
4 Project: Build a Multi-Source RAG System with Query Routing
35min
1 From Traditional ML to Transfer Learning — Understanding the Evolution
Preview 24min
2 LoRA and QLoRA Explained — Efficient Fine-Tuning for Everyone
28min
3 Preparing Training Data — Dataset Curation for Fine-Tuning
26min
4 Project: Fine-Tune Llama 3 on Custom Data with QLoRA
35min
1 Advanced Fine-Tuning — DPO, RLHF, and Preference Alignment
28min
2 Training Infrastructure — GPUs, Cloud, and Cost Optimization
24min
3 Evaluation and Iteration — Measuring Fine-Tuning Success
24min
4 Project: Build a Domain-Specific Model That Rivals GPT-4
35min
1 Agent Architecture — ReAct, Tool Use, and Reasoning Loops
Preview 28min
2 Building Agents with LangGraph — Stateful Multi-Step Workflows
30min
3 Multi-Agent Orchestration — CrewAI and Agent Teams
26min
4 Project: Build an Autonomous Deal-Finding Agent System
35min
1 Deploying LLM Applications — Docker, APIs, and Scaling
28min
2 Monitoring, Cost Optimization, and Guardrails
26min
3 Capstone Project: Full-Stack AI Application
40min
4 Career Guide — Building Your AI Engineering Portfolio
22min
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