What Makes This Bootcamp Different?
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100% LIVE, instructor-led sessions every weekend. Real-time interaction, Q&A, and doubt clearing.
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Covers the FULL AI engineering spectrum, from LLM fundamentals, embeddings & vector databases to RAG, agents, multi-agent systems, fine-tuning, context engineering, cost optimisation, and cloud deployment. This isn’t just an agentic framework course
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Designed and taught by AI industry experts & engineering leaders with real-world experience building and shipping AI systems at scale
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Master the complete modern AI stack: Python, FastAPI, LangChain, LangGraph, DSPy, MCP, Qdrant, LangSmith, AWS, Unsloth, Ollama & more
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Production-first mindset: Cost optimisation, caching, rate limiting, model routing & scaling the engineering challenges that matter in real products
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LLM Fine-Tuning with LoRA/QLoRA + running models locally with Ollama go beyond API wrappers
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Context Engineering & MCP (Model Context Protocol), the newest paradigms that top AI teams are adopting right now
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Guardrails, evaluations, adversarial attacks & compliance - build AI that’s safe for production
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Capstone project with architectural guidance + live project showdown in front of experts
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Live interview prep tips with industry AI leaders to prepare you for real AI engineering interviews
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Integrated soft skills training: AI Product Thinking, Personal Branding, Stakeholder Management, Time Management & Deep Work
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Practical job assistance: Resume building, LinkedIn optimization, interview preparation, everything you need to land the AI engineer role
Hear It From
Our Happy Learners
Our content is rated 4.9/5 from 24934+ Learners
Python
I just finished a SQL course and was really impressed. It did a great job of breaking things down, starting with the basics and gradually moving into more complex stuff like joins and subqueries. The hands-on exercises and real-world examples made it easy to stay engaged and actually learn by doing. Overall, it gave me a solid foundation and I am confident now to apply these skills into real world projects. If you're new to SQL or just want to brush up, I'd definitely recommend it.
Landed a Job
It was a very helpful course that gave me a lot of information and hands-on experience, and I learned a lot about the basics and projects. it will be a good start to a career for anyone. I look forward to enrolling in a few more courses and upskilling myself more.
Thank you.
Landed a Job
Completing this course has been an incredibly rewarding experience. Taught by Dhaval Patel, the Deep Learning: Beginner to Advanced course stands out for its clarity, structure, and practical depth.
From the fundamentals of neural networks to the intricacies of CNNs, RNNs, and Transformers, every concept is explained in a way that is accessible yet technically solid. What sets this course apart is the strong focus on hands-on learning — using PyTorch to build real-world projects that reinforce the theory.
Key strengths of the course:
✅ Clear and intuitive explanations
✅ Strong emphasis on real-world applications
✅ Step-by-step PyTorch implementation
✅ Coverage of cutting-edge topics like Transformers
✅ Practical tips and insights drawn from industry experience
Whether you're new to deep learning or looking to strengthen your foundation, this course is an excellent investment of time and effort. I walked away not just with knowledge, but with confidence to apply deep learning in real projects.
A big thank you to Dhaval Patel and the Codebasics team for making deep learning approachable and applicable.
Just finished this awesome course! It was clear, engaging, and packed with useful info. The lessons were short and easy to understand, making learning fun and efficient. Highly recommend to anyone looking to improve their skills!
Math & Stats
Landed a Job
Excellent!! I learn a lot from this course. I am working professionally; this course helps me upskill myself and also clear my doubts regarding the machine learning and Statistical knowledge.
Machine Learning
Landed a Job
Overview
What you'll learn in
this Live AI Engineering for Software Engineers Bootcamp
Week-1: AI Foundations & LLM Fundamentals
Python & LLM Basics
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Session 1 – Kickoff and Fundamentals
AI Landscape · Python Fundamentals (functions, file manipulation, classes & objects) · APIs & Decorators ·
Output: Your First LLM-Powered App (Streamlit + Groq)
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Session 2 – LLMs, Embeddings & Transformer Architecture
How does an LLM work? · Transformer Architecture · Embeddings & Semantic Similarity · Key Parameters in LLMs
Output: Embedding & Semantic Similarity in depth Understanding
Week-2: Vector DB, RAG & Retrieval Foundations
LLMs & Vector DBs
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Session 3 – Vector DBs & RAG
Introduction to VectorDB · Qdrant Hands-on · RAG in Pure Python · Qdrant Advanced Ops
Output: Your First RAG Pipeline
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Session 4 – LangChain, Docling & Chunking Strategies
LangChain Intro · RAG in LangChain · Docling Document Parsing · Hierarchical Chunking
Output: LangChain RAG Pipeline with Smart Chunking
Week-3: Advanced RAG & Agentic AI
RAG Engineering
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Session 5 – Advanced RAG Techniques & Hands-On
Vector & Vectorless RAG Architectures · Hybrid RAG · SQL RAG · Graph RAG
Output: Advanced RAG, Hybrid Search, Reranking
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Session 6 – Agentic AI Foundations
What are AI Agents? · Tool Calling & ReAct Loop · Building your first Agent with LangChain · Routing with Semantic Router · Memory in Agents
Output: Your First Tool-Calling Agent
Week-4: Agent Orchestration & Multi-Agent Systems
Agentic AI
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Session 7 – Agent Orchestration & LangGraph
Introduction to LangGraph · Workflows using LangGraph · Conditional branches & loops · Build a ReAct Loop using LangGraph
Output: ReAct Agent Rebuilt in LangGraph
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Session 8 – Multi Agent Systems
Introduction to Multiagent Systems · Subagents and hand-offs · Multi-Agent Architectures
Output: GitHub Agent - Multiagent system for GitHub automation
Week-5: Multi-Agent Systems & Evaluation
Multi-Agent & Evals
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Session 9 – Evals, Guardrails & Observability
Online vs Offline Evals · Evaluating RAG Pipelines · Evaluating Agents · Guardrails · Interpretation of Evals
Output: Adding Eval harness to the projects.
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Session 10 – Multimodal AI
Architecture of a multimodal AI model · Extract data from PDFs/images · Multimodal RAG System
Output: Multimodal RAG Extracting Structured Data from Documents
Week-6: Multimodal AI & Cloud Deployment
Multimodal & AWS
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Session 11 – Deployment on AWS
Introduction to AWS's Agent Stack · Deploying LangChain on AgentCore · AgentCore Services
Output: Your Agent Deployed on AWS AgentCore
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Session 12 – MCP
What is Model Context Protocol · MCP Architecture · Building MCP Servers · Building MCP Clients
Output: Your First MCP Server & Client
Week-7: MCP & Context Engineering
MCP & Context
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Session 13 – Context Engineering
Importance of Context in complex AI Systems · Context Engineering Fundamentals · Improving Context Propagation in your agents · Hands-on with deepagents
Output: Compound AI system (LangGraph multi-agent + MCP server + RAG) code & LangSmith
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Session 14 – Fine Tuning & Local AI
Fine-tune vs RAG: when to opt for what? · LoRA & QLoRA training · Synthetic dataset pipelines · What are SLMs? · Ollama
Output: Fine-Tuned SLM Running Locally with Ollama
Week-8: Fine-Tuning & Capstone Kickoff
Fine-Tuning
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Session 15 – Capstone Project Discussion & Assignment Showcase
Scoping and discussing capstone project ideas · Showcase of assignments/side projects completed so far
Output: Your Capstone Project Plan
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Session 16 – AI Product Thinking and Reinforcement Learning
Turning AI capabilities into usable, valuable products · Thinking like an AI product builder, not just a model user · Core intuition behind reinforcement learning · Reward design for better behaviour
Output: Product Thinking & Reinforcement Learning Notes
Week-9: AI Product Thinking & Security
Product & Security
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Session 17 – Advanced AI Engineering & Security
Cost optimization at scale · Semantic caching · Smart model routing · OWASP Top 10 for AI · Defending your system from adversarial attacks
Output: AI Security & Cost Optimization Playbook
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Session 18 – Stakeholder Management and System Design for AI
Managing expectations of stakeholders · How an AI System is different · AI System Design Patterns · Production caveats of an AI System
Output: AI System Design Patterns Reference
Week-10: System Thinking & Voice AI
System Design & Voice
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Session 19 – Voice AI
Real-time voice stack · Low-latency voice systems · Interruptions & silence handling · Multi-turn context recovery
Output: Voice agent (<800ms end-to-end, tested on 10 queries)
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Session 20 – Inference Engineering
What is Inference Engineering · Runtime Optimizations: batching, caching, quantization, speculation · Infrastructure Optimizations: routing, load balancing, autoscaling · Serving an LLM Endpoint with vLLM
Output: Local SLM (Qwen 2.5-3B) as a production REST API with vLLM, plus a cost/latency router benchmark
Week-11: Inference Engineering & Personal Branding
Inference & Career Guidance
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Session 21 – Personal Branding & Mock Interview
LinkedIn & Twitter positioning · GitHub portfolio storytelling · Live Mock Interview with Dhaval Patel & Siddhant Pandey
Output: Personal Brand & Interview Readiness Checklist
Week-12: Final Project Showcase
Capstone
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Session 22 – Final Project Showcase
Presenting your capstone project to peers, mentors & industry guests
Output: Capstone Demo Day
The Codebasics Promise
Premium Training, Without The Premium Price.
Other paths can work. Here is exactly what you get from each.
| What you get | Self-study | Other live bootcamps | Our live cohort |
|---|---|---|---|
| Live, mentor-led sessions | |||
| Real projects shipped to GitHub | Rarely | Sometimes | 6+1 projects |
| The current 2026 agent stack | On your own | Often dated | |
| Job assistance | |||
| Investment | Your time | ₹2,00,000+ | US$660 |
May we help you?
Frequently Asked
Questions
Q.1
Do I also get the AI Engineering & Data Science Bootcamp 4.0?
Q.2
When are the live sessions?
Q.3
When does Cohort 3 officially launch?
Q.4
What if I miss a live session?
Q.5
What happens after the 75 days? Do I lose access?
Q.6
What is the Inner Circle, and how is it different from regular enrolment?
Q.7
When do I get access after enrolling as an Inner Circle member?
Q.8
What is the difference between AI Engineering and Data Science Bootcamp 4.0 and the AI Engineering Bootcamp?
AI Engineering Bootcamp is a fast-paced, live cohort for experienced software engineers with 2+ year of experience, that assumes strong programming fundamentals and focuses on AI architecture, system thinking, production engineering & shipping real-world AI systems.
Q.9
What if I have already bought the AI Engineering and Data Science Bootcamp 4.0?
Q.1
Do I need ML experience?
Q.2
I'm a fresher or have less than 2 years of experience. Can I join?
Q.3
Who is this bootcamp designed for?
Q.1
Is there job assistance?
Q.1
I already own the AI Engineering & DS Bootcamp 4.0. What do I pay?
Q.2
Can I purchase only the AI Engineering Bootcamp without the AI Engineering & DS Bootcamp 4.0?
Q.1
I used a subsidy (my existing AI Engineering & DS Bootcamp 4.0 or individual course purchase). Can I refund my original purchase after enrolling?
Q.2
I used a subsidy and now want to refund the AI Engineering Bootcamp itself. What happens?
Q.3
What's the refund policy for Cohort 3 post-launch enrollees?
Q.4
What's the refund policy for Inner Circle members?
Q.1
What do I do on weekdays, after the weekend live sessions?
Q.2
How do I get help if I'm stuck?
Q.1
What system configuration do I need?
• OS: Windows 11
• Processor: Intel Core i7 (10th Gen+) or AMD Ryzen 7 (4th Gen+). An i5 works if you're not focused on local model training.
• RAM: 8GB minimum, 16GB recommended
• Storage: 512GB SSD strongly recommended
• GPU: NVIDIA GTX 1660 or higher for deep learning and GPU-accelerated tasks
This covers all bootcamp work comfortably. You'd only need stronger hardware if you plan to fine-tune small LLMs locally.
Q.1
Do I also get the AI Engineering & Data Science Bootcamp 4.0?
Q.2
When are the live sessions?
Q.3
When does Cohort 3 officially launch?
Q.4
What if I miss a live session?
Q.5
What happens after the 75 days? Do I lose access?
Q.6
What is the Inner Circle, and how is it different from regular enrolment?
Q.7
When do I get access after enrolling as an Inner Circle member?
Q.8
What is the difference between AI Engineering and Data Science Bootcamp 4.0 and the AI Engineering Bootcamp?
AI Engineering Bootcamp is a fast-paced, live cohort for experienced software engineers with 2+ year of experience, that assumes strong programming fundamentals and focuses on AI architecture, system thinking, production engineering & shipping real-world AI systems.
Q.9
What if I have already bought the AI Engineering and Data Science Bootcamp 4.0?
Q.1
Do I need ML experience?
Q.2
I'm a fresher or have less than 2 years of experience. Can I join?
Q.3
Who is this bootcamp designed for?
Q.1
Is there job assistance?
Q.1
I already own the AI Engineering & DS Bootcamp 4.0. What do I pay?
Q.2
Can I purchase only the AI Engineering Bootcamp without the AI Engineering & DS Bootcamp 4.0?
Q.1
I used a subsidy (my existing AI Engineering & DS Bootcamp 4.0 or individual course purchase). Can I refund my original purchase after enrolling?
Q.2
I used a subsidy and now want to refund the AI Engineering Bootcamp itself. What happens?
Q.3
What's the refund policy for Cohort 3 post-launch enrollees?
Q.4
What's the refund policy for Inner Circle members?
Q.1
What do I do on weekdays, after the weekend live sessions?
Q.2
How do I get help if I'm stuck?
Q.1
What system configuration do I need?
• OS: Windows 11
• Processor: Intel Core i7 (10th Gen+) or AMD Ryzen 7 (4th Gen+). An i5 works if you're not focused on local model training.
• RAM: 8GB minimum, 16GB recommended
• Storage: 512GB SSD strongly recommended
• GPU: NVIDIA GTX 1660 or higher for deep learning and GPU-accelerated tasks
This covers all bootcamp work comfortably. You'd only need stronger hardware if you plan to fine-tune small LLMs locally.
Inner circle price benefit extended until 12th Aug. Save US$180
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