Become an AI Engineer — Build LLMs, Agents & Real AI Systems
Gain the skills top companies need: build RAG-based systems, fine-tune open-source models, and deploy real AI applications with MLOps.
Level 3
AI Engineering - LLMs & Agents
Career outcome: AI Engineer, GenAI Engineer, LLM
Engineer
Engineer
Prerequisites
- Python
- Python fundamentals
- Python data structures (lists, dictionaries, sets, tuples)
- List/dictionary comprehensions and functional programming tools
- OOP principles in Python (classes, inheritance, encapsulation)
- Virtual environments (venv, conda)
- Package management
- Git (branching, merging, pull requests)
- IDE (VSCode or another IDE of your preference)
- 15-20 hours/week commitment
- Data Science (L1) and Deep Learning (L2) Knowledge are required
If you are unfamiliar with some of these topics or want to refresh your knowledge,
these free resources linked below cover all prerequisites
these free resources linked below cover all prerequisites
If you’re a complete beginner in programming, start with one of these:
- AI Python for Beginners course by DeepLearning.AI (~10h)
- Intro to Programming and Python courses by Kaggle (~10h)
- Python tutorial by Spark Code Hub
- A Byte of Python book by Swaroop Chitlur
- learnpython.org interactive tutorial by Ron Reiter
- Python for Beginners tutorial by Beau Carnes
- Object Oriented Programming with Python tutorial by JimShapedCoding
- Intermediate Python tutorial by Patrick Loeber
- Python Virtual Environments: A Primer by Martin Breuss
- Using Python's pip to Manage Your Projects' Dependencies by Philipp Acsany
For a basic understanding, check one of these introductory resources:
- Git & GitHub Crash Course by Kalob Taulien (Udemy)
- GitHub and Git Tutorial for Beginners by Abid Ali Awan (DataCamp)
- Pro Git book by Scott Chacon and Ben Straub (check sections: Getting Started, Git Basics, Git Branching, Distributed Git, GitHub)
- Atlassian Git tutorials (check sections: Beginner, Getting started, Collaborating workflows)
Official tutorials for Visual Studio Code:
Machine Learning Path — 3 Levels to Master AI
Machine Learning is a journey with three levels:
- Level 1: Data ScienceLearn how to understand and organize data so computers can make sense of it.
- Level 2: Classical ML MasteryTeach computers to learn from data and make smart predictions.
- Level 3: LLMs & AgentsStep into the world of modern AI, where machines can think, talk, and act on their own.
Course Program
Month 1: Rise of the Giants: The LLM Revolution
Harness the power of Large Language Models (LLMs) and modern AI
Building modern AI applications with Retrieval-Augmented Generation (RAG) and Parameter-Efficient Fine-Tuning (PEFT)
AI Travel Explorer - RAG System
■ You'll build the core knowledge system for your "AI Travel Explorer"
project. This system will "read" real travel guides and answer complex,
conversational questions.
Function Calling Fine-Tuning
■ You'll dive into modern LLM customization, using the Low-Rank
Adaptation (LoRA) technique to teach an open-source LLM to become
a specialist at converting plain English questions into accurate
■ You'll build the core knowledge system for your "AI Travel Explorer"
project. This system will "read" real travel guides and answer complex,
conversational questions.
Function Calling Fine-Tuning
■ You'll dive into modern LLM customization, using the Low-Rank
Adaptation (LoRA) technique to teach an open-source LLM to become
a specialist at converting plain English questions into accurate
- structured function calls for your travel agent project.
RAG, LangChain, Vector Databases, LLM APIs, LLM Fine-Tuning, LoRA,
PEFT, Function Calling, Prompt Engineering.
PEFT, Function Calling, Prompt Engineering.
Month 2: The Future Is Now: AI Agents
Create your own AI agents
Mastering Agentic workflow.
Building an AI Agent - The Tool-User
■ You will enhance your "AI Travel Explorer" with its first agentic
capability.
■ You will enhance your "AI Travel Explorer" with its first agentic
capability.
Function Calling, AI Agents, Agentic workflows, ReAct, LangChain.
Month 3: Move To Prod
Explore MLOps practices and dive into the ML lifecycle
Learning the essential MLOps skills and tools.
MLOps in Practice - Deploying a Model
■ You'll create an end-to-end pipeline for model training, evaluation, and
deployment.
■ You'll create an end-to-end pipeline for model training, evaluation, and
deployment.
MLOps, CI/CD for ML pipelines, ML lifecycle, System Integration,
Productization.
Productization.
🗓️ Month 1: Tabular Data Mastery & Foundations
- Focus: Mastering the data science toolkit, EDA, and core regression models.
- Project: House Prices - Advanced Regression Techniques
- You'll build models to predict home values, mastering feature engineering and regression on a complex, real-world Kaggle dataset.
- (Skills: EDA, Feature Engineering, Regression Models, XGBoost, Kaggle Competitions)
Main Project Goal: Build the v0.1 command-line version with simple, rule-based responses.
Our YouTube Channel
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We believe in sharing knowledge, so on our channel, you'll find:
Student success stories
Tutorials and useful materials for aspiring QA engineers
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With over 1 million views, our channel has become a trusted resource for learning and growth in the QA industry.
Student Success Stories
Nothing speaks louder than real success stories. Our students come from different backgrounds, but they all have one thing in common — they successfully transitioned into QA careers after completing our program.
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If the course isn’t right for you, we offer a full refund within 14 days after the course starts.
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