College Students
Build a strong AI portfolio before graduation and position yourself for high-value AI roles from day one.
GenAI Engineer
Go from Python fundamentals to shipping production Generative AI apps — LLM APIs, prompt engineering, LangChain, vector databases and RAG.
Upcoming Batch25 Sept 2026
- Duration14+ weeks (120+ hours)
- Hands-on15+ projects · 11 modules
- CertificateVerified certificate to boost your Resume
What you'll learn
The concrete skills you'll walk away with
Python for AI and Data Science
Object-Oriented Programming in Python
NumPy, Pandas and Matplotlib for data workflows
Fundamentals of Generative AI and LLMs
Working with LLM APIs (OpenAI, Gemini, Claude, Hugging Face)
Prompt Engineering (Zero/One/Few Shot, CoT, ReAct)
LangChain framework — chains, memory, retrieval
Vector Databases (FAISS, ChromaDB, Pinecone)
Retrieval Augmented Generation (RAG) systems
Cost optimization and best practices for LLM apps
Production-grade GenAI application deployment
Curriculum
What you'll actually build, week by week
Each module is designed to end with something you've built — not just something you've watched.
What you'll learn
- Introduction to Python
- Installing Python and IDE Setup
- Python Syntax
- Variables and Data Types
- Operators
- Input and Output
- Type Casting
- Conditional Statements
- Looping Statements
What you'll learn
- Functions
- Strings
- Lists
- Tuples
- Sets
- Dictionaries
- List Comprehensions
- Modules and Packages
- Exception Handling
What you'll learn
- Introduction to Object-Oriented Programming
- Classes and Objects
- Constructors
- Instance and Class Variables
- Instance, Class and Static Methods
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction
- Magic Methods
What you'll learn
- Introduction to NumPy
- NumPy Arrays and Operations
- Introduction to Pandas
- Series and DataFrames
- Data Import and Export
- Data Cleaning and Preprocessing
- Data Manipulation and Analysis
- Introduction to Matplotlib
- Data Visualization
- Exploratory Data Analysis (EDA)
What you'll learn
- What is Generative AI
- Evolution of Generative AI
- How Generative AI Works
- Types of Generative Models
- Foundation Models
- Large Language Models (LLMs)
- Transformer Architecture
- Tokens and Tokenization
- Embeddings
- Context Window
- Inference
- AI Model Lifecycle
- Popular GenAI Models
- Open Source vs Closed Source Models
See all 11 modules · 150 hours of content
14+
Weeks
120+
Hours
15+
Projects
11
Modules
Hands-on experience
You'll ship real projects, not tutorial exercises
15+Projects
Build Production-Ready AI Applications
Move beyond theory by building practical applications throughout the program. Every module includes guided implementation, helping you gain hands-on experience with modern LLMs, RAG systems, vector databases, and AI application development.
Learn by BuildingWhat you'll build across
- Python Development
- Generative AI
- Large Language Models
- Prompt Engineering
- LLM APIs
- LangChain
- RAG Systems
- Vector Databases
- Semantic Search
- AI Applications
Industry-Oriented
Real-world problem solving
Real-World Use Cases
Job-ready applications
Portfolio-Ready
Build projects that stand out
Guided Implementation
Step-by-step learning path
Capstone Project
End-to-end AI application
Your Journey
Your Path from Student to GenAI Engineer
A step-by-step journey to transform your skills and build a successful career in Generative AI.
Learner
Start your Generative AI journey with Python basics and core programming skills.
Python Developer
Master Python, OOPs and data handling for building AI-powered applications.
GenAI Practitioner
Understand LLMs, prompt engineering and create intelligent AI interactions.
LLM Application Developer
Integrate LLM APIs and build applications using LangChain and modern tools.
RAG Engineer
Build RAG systems using embeddings, vector databases and semantic search.
GenAI Engineer
Build production-ready GenAI applications and solve real-world business problems.
Roles you'll be ready for
Roles this program can help you prepare for:
- GenAI Engineer
- LLM Application Developer
- RAG Engineer
- Prompt Engineer
- Generative AI Developer
- AI Application Developer
- AI Solutions Engineer
- AI Integration Engineer
- Conversational AI Developer
- AI Automation Developer
- LLM Engineer
- AI Product Engineer
Who this is for
This program is designed for ambitious learners
Freshers
Start your career with one of the most in-demand skill sets in the technology industry.
Working Professionals
Expand your expertise and transition into AI Engineering, Agentic AI, or LLM Application Development.
Software & Python Developers
Leverage your existing programming skills to build intelligent AI applications and autonomous agents.
QA Engineers
Learn to build AI-powered testing tools and automated workflows using LLMs and agentic systems.
DevOps Engineers
Expand from infrastructure to AI infrastructure, LLMOps, and intelligent automation pipelines.
Data Professionals
Progress from data analysis to building LLM-powered data applications and knowledge retrieval systems.
Career Switchers & AI Enthusiasts
Whether changing careers or exploring AI for the first time, this program provides a structured foundation to launch your AI journey.
Frequently asked questions
Common questions from
real students
Still curious? Email us and one of our mentors will personally get back to you within a day.
College students, freshers, working professionals, software and Python developers, QA and DevOps engineers, data professionals and career switchers who want to move into GenAI Engineering or LLM Application Development.
No prior AI experience is required. The program begins with Python foundations and gradually progresses through Generative AI, LLM APIs, LangChain, Vector Databases and RAG. Basic computer familiarity is enough.
GenAI Engineer is a focused 14-week track that ends at production RAG applications. The Mastery program extends further into Agentic AI — LangGraph, AI Agents, MCP and Multi-Agent Systems — over a longer 22+ week schedule.
Python, NumPy, Pandas, Matplotlib, OpenAI, Google Gemini, Anthropic Claude, Hugging Face, LangChain, FAISS, ChromaDB and Pinecone — the applied GenAI engineering stack.
The program spans 14+ weeks with 120+ hours of guided learning — roughly 7-9 hours per week between live sessions and hands-on practice on 15+ real-world projects.
Yes — every learner who completes the program receives a Certificate of Completion that can be shared on LinkedIn and included in your professional portfolio.
Ready to start your learning journey?
Join our growing community of successful graduates