- For working developers
- No AI experience needed
- Self-paced
- Hands-on projects
Learn AI Engineering.From First Principles to Production.
For developers who already ship software and want to build real AI systems. Understand how LLMs work, then build a production RAG application and agent, with evals, security, cost control and deployment.
₹2,999₹4,999
Lifetime access for early buyers · 7-day refund, no questions asked
Video lessons · Illustrated notes · Python demos · Quizzes · Capstone project
See how you'll learn
Watch the opening of the first lecture before you decide.
▶ 5-minute previewLecture 1 · Foundations
Tokens: how LLMs actually read text
LLMs never see raw text, only tokens. Watch how a tokenizer breaks words apart with Byte Pair Encoding, and why that decides what you pay.
- ✓Why words split in strange places
- ✓Why Hindi and other languages cost more
- ✓Why ChatGPT, Claude and DeepSeek tokenize differently
What you'll learn
Understand the internals
Tokens, embeddings, attention, transformers, and inference, explained visually.
Build real AI applications
Implement RAG, vector search, tool-using agents, and a complete capstone project.
Learn production engineering
Evaluate reliability, handle security risks, optimize costs, and deploy your application.
Meet your instructor
Learn Applied AI from someone who's built it at scale.
Sameer Bhardwaj
Former Software Engineer at Google & Flipkart · Founder of Layrs
I've spent years building production software systems, from large-scale infrastructure at Google to real-time voice AI agents at Layrs.
At Layrs, I engineered AI systems that handled 100K+ conversation minutes and approximately 1 billion tokens per month.
This course brings together everything I've learned about how LLMs actually work, how to build with them, and what it takes to make AI applications reliable in production.
- Voice AI minutes/month
- 100K+
- LLM tokens/month
- ~1B
- Engineering experience
- Google + Flipkart
The course
Frequently asked questions
Who is this course for?
Developers who already ship software and want to build real AI systems. You don't need any AI or machine-learning background: everything from tokens to transformers is explained from first principles. You do need to be comfortable writing code.
What do I need to know before starting?
The demos are in Python, so being comfortable reading and running basic Python code will help. Everything else, from tokens and embeddings to transformers, is explained from first principles.
What will I be able to build by the end?
RAG applications with vector search, tool-using AI agents, and a complete capstone project, along with the skills to evaluate, secure, optimize, and deploy them.
How is the course structured?
Five modules: Foundations, Prompting & Retrieval, Measuring & Improving, Agents, and Production. Each lesson pairs a video lecture with illustrated notes and Python demos, and quizzes check your understanding along the way.
Is it self-paced?
Yes. Work through lessons whenever suits you. Your completed lessons and quiz scores are saved, so you can pick up where you left off.
Will this make me a professional AI engineer?
It gives you a solid foundation: how the models work, and hands-on experience building and shipping AI applications. Becoming job-ready also takes continued practice and building your own projects, and the capstone is a strong place to start.