About Course

About Course

The AI Engineering & Agentic AI Program is a comprehensive, placement-focused training program designed to transform graduates into industry-ready AI Engineers, GenAI Developers, and Agentic AI Professionals.

This program combines Python programming, software engineering, machine learning fundamentals, Large Language Models (LLMs), Prompt Engineering, Retrieval Augmented Generation (RAG), Agentic AI, LangGraph, Memory Systems, Knowledge Graphs, Model Context Protocol (MCP), Cloud Deployment, and Production AI Engineering.

Designed with guidance from industry experts and aligned with current market demands, the program focuses on hands-on learning, real-world projects, and practical implementation of modern AI technologies used by leading organizations worldwide.

Students will build multiple industry-oriented projects, develop production-ready AI applications, and create a professional portfolio to showcase their skills to potential employers.

Who Should Enroll

• Fresh Graduates seeking careers in AI and Generative AI

• Software Developers transitioning into AI Engineering

• Python Developers looking to specialize in AI

• Data Analysts interested in AI-powered solutions

• IT Professionals exploring Agentic AI technologies

• Students aspiring to become AI Engineers, GenAI Developers, or LLM Application Developers

Module 1 – Python Programming & Software Development Fundamentals

Topics Covered

Introduction to Programming & Python

• Programming Fundamentals

• Python Installation & Environment Setup

• Variables, Data Types & Operators

• Input and Output Operations

• Writing Python Programs

Core Python Programming

• Strings

• Lists

• Tuples

• Sets

• Dictionaries

• Data Manipulation Techniques

Control Flow & Problem Solving

• Conditional Statements

• Loops

• Pattern-Based Programming

• Logical Problem Solving

• Coding Challenges

Functions & Modular Programming

• Functions

• Arguments & Parameters

• Return Values

• Scope & Namespace

• Lambda Functions

• Modules & Packages

Object-Oriented Programming

• Classes & Objects

• Constructors

• Encapsulation

• Inheritance

• Polymorphism

• Abstraction

• Static & Class Methods

Advanced Python

• Comprehensions

• Generators & Iterators

• Decorators

• File Handling

• Exception Handling

• Introduction to Concurrency

• Async Programming

• Pydantic Basics

Hands-on

• Python Programming Exercises

• Number & String Manipulation Programs

• Inventory Management System

• Student Management Application

• Banking System Simulation

• OOP-Based Projects

• File Processing Applications

Tools & Technologies Covered

• Python

• VS Code

• Pydantic

Expected Outcomes

Students will be able to:

• Write efficient Python programs

• Apply object-oriented programming principles

• Develop modular applications

• Handle exceptions and file operations

• Build a strong foundation for AI engineering

Module 2 – Software Engineering, Databases & API Development

Topics Covered

Software Development Fundamentals

• SDLC Concepts

• Agile Methodology

• Software Development Best Practices

• Code Documentation

Git & GitHub

• Version Control Fundamentals

• Git Commands

• Branching & Merging

• Pull Requests

• GitHub Workflows

Testing & Quality Engineering

• Unit Testing with Pytest

• Test Fixtures

• Mocking Concepts

• API Testing Fundamentals

• Test Automation Basics

CI/CD Fundamentals

• Continuous Integration

• Continuous Deployment

• GitHub Actions

• Automated Testing Pipelines

• Deployment Workflows

Database Fundamentals

• Relational Databases

• SQL Basics

• CRUD Operations

• Joins

• Aggregations

• Subqueries

API Fundamentals

• REST APIs

• HTTP Methods

• JSON

• API Design Concepts

• API Testing

FastAPI Development

• FastAPI Fundamentals

• Request Validation

• Response Models

• Authentication Basics

• CRUD Application Development

Hands-on

• SQL Query Exercises

• Database Design Assignments

• GitHub Collaboration Exercises

• API Testing Projects

• FastAPI CRUD Application

• GitHub Actions Pipeline Setup

Tools & Technologies Covered

• Git

• GitHub

• SQL

• Pytest

• GitHub Actions

• FastAPI

• JSON

Expected Outcomes

Students will be able to:

• Work confidently with databases

• Build REST APIs using FastAPI

• Implement testing strategies

• Create CI/CD pipelines

• Follow modern software development workflows

Module 3 – Artificial Intelligence, Machine Learning & Large Language Models

Topics Covered

AI Fundamentals

• Artificial Intelligence

• Machine Learning

• Deep Learning

• Generative AI

• AI Use Cases

Machine Learning Foundations

• Supervised Learning

• Unsupervised Learning

• Regression

• Classification

• Clustering

• Model Evaluation Techniques

Deep Learning Fundamentals

• Neural Networks

• Activation Functions

• Training Concepts

• Backpropagation Fundamentals

• Deep Learning Applications

NLP Foundations

• Natural Language Processing

• Text Processing

• Language Understanding

• Language Generation

Understanding LLMs

• Evolution of LLMs

• GPT Architecture

• Transformer Architecture

• Attention Mechanism

• Context Windows

Embeddings & Semantic Search

• Vector Embeddings

• Similarity Search

• Semantic Understanding

• Vector Database Concepts

Modern AI Platforms

• OpenAI Models

• Gemini Models

• Hugging Face Models

• Open Source LLMs

• Model Selection Strategies

Hands-on

• ML Model Development Exercises

• Classification Projects

• Clustering Projects

• Tokenization Exercises

• Embedding-Based Similarity Search

• LLM Evaluation Activities

Tools & Technologies Covered

• OpenAI

• Gemini

• Hugging Face

• Scikit-Learn

Expected Outcomes

Students will be able to:

• Understand AI and ML fundamentals

• Explain how LLMs work

• Work with embeddings and semantic search

• Select appropriate AI models

• Apply machine learning techniques to real-world problems

Module 4 – Prompt Engineering & AI Application Development

Topics Covered

Prompt Engineering Fundamentals

• Prompt Design Principles

• Zero-Shot Prompting

• One-Shot Prompting

• Few-Shot Prompting

Advanced Prompt Engineering

• Chain of Thought Prompting

• Persona Prompting

• Structured Prompting

• Prompt Evaluation

Working with AI APIs

• OpenAI API Integration

• Gemini API Integration

• Function Calling

• Structured Outputs

• API Optimization

Open Source AI Development

• Ollama

• OpenWebUI

• Hugging Face Models

• Local LLM Deployment

Building AI Applications

• AI Chat Applications

• Content Generation Solutions

• Document Processing Systems

• Business Automation Applications

Hands-on

• AI Chatbot Development

• Content Generation Application

• AI Document Analyzer

• Function Calling Projects

• Local LLM Deployment

Tools & Technologies Covered

• OpenAI API

• Gemini API

• Ollama

• OpenWebUI

• Hugging Face

Expected Outcomes

Students will be able to:

• Design effective prompts

• Integrate AI APIs into applications

• Build AI-powered business solutions

• Deploy and utilize local LLMs

Module 5 – Retrieval Augmented Generation (RAG) & Agentic AI

Topics Covered

Introduction to RAG

• RAG Fundamentals

• Enterprise Knowledge Systems

• RAG Architecture

• Information Retrieval Concepts

Building RAG Systems

• Document Loading

• Chunking Strategies

• Embeddings Generation

• Vector Storage

• Retrieval Techniques

LangChain Fundamentals

• LangChain Architecture

• Chains

• Prompts

• Retrievers

• Memory Concepts

AI Agents Fundamentals

• AI Agent Concepts

• Agent Components

• Agent Planning

• Agent Decision Making

Building Agentic AI Solutions

• Tool Calling

• Function Calling

• Coding Agents

• Research Agents

• Automation Agents

• Multi-Agent Systems

Fine-Tuning LLMs

• Fine-Tuning Concepts

• Instruction Tuning

• LoRA

• QLoRA

• Fine-Tuning Workflows

• Model Evaluation

Hands-on

• Enterprise Knowledge Chatbot

• PDF Question Answering System

• Document Search Engine

• Research Assistant Agent

• Coding Assistant Agent

• Multi-Agent Workflow Project

Tools & Technologies Covered

• LangChain

• Vector Databases

• OpenAI

• Gemini

Expected Outcomes

Students will be able to:

• Build enterprise RAG applications

• Develop intelligent AI agents

• Create multi-agent workflows

• Fine-tune language models

• Integrate external tools into AI systems

Module 6 – Advanced Agentic AI & Production Systems

Topics Covered

LangGraph Fundamentals

• Graph-Based Workflows

• Nodes & Edges

• State Management

• Conditional Routing

• Workflow Orchestration

AI Memory Systems

• Short-Term Memory

• Long-Term Memory

• Episodic Memory

• Semantic Memory

• Mem0 Integration

Graph Memory & Knowledge Graphs

• Knowledge Graph Fundamentals

• Neo4j

• Cypher Queries

• Graph-Based Retrieval

Conversational & Voice AI

• Speech-to-Text

• Text-to-Speech

• Voice Agents

• Conversational AI Systems

Model Context Protocol (MCP)

• MCP Fundamentals

• MCP Architecture

• MCP Clients & Servers

• Tool Integrations

Production AI Architectures

• Redis

• Queue Systems

• Worker Architecture

• Background Processing

• Scalable AI Systems

Hands-on

• LangGraph Agent Development

• Memory-Enabled AI Agent

• Knowledge Graph Assistant

• Voice-Based AI Assistant

• MCP Integration Project

• Queue-Based AI Processing System

Tools & Technologies Covered

• LangGraph

• Mem0

• Neo4j

• Redis

• MCP

Expected Outcomes

Students will be able to:

• Build advanced AI agent workflows

• Implement memory-enabled AI systems

• Create voice-based AI assistants

• Develop scalable AI architectures

Module 7 – Deployment, Capstone Projects & Placement Readiness

Topics Covered

Docker & Containerization

• Docker Fundamentals

• Docker Images & Containers

• Networking

• Volumes

• Docker Compose

AWS for AI Engineers

• AWS Fundamentals

• EC2

• S3

• Lambda

• IAM Basics

Cloud Deployment

• Deploying AI Applications on AWS

• Environment Management

• Container Deployment

• Production Hosting

Monitoring & Logging

• CloudWatch Fundamentals

• Logging Concepts

• Monitoring Concepts

• Performance Optimization

Placement Readiness Program

• Resume Building

• LinkedIn Profile Optimization

• GitHub Portfolio Development

• AI Interview Preparation

• Mock Interviews

• Placement Preparation

Hands-on

• Dockerizing AI Applications

• Deploying AI Solutions on AWS

• Monitoring AI Applications

• Portfolio Development

• Resume Creation

• Mock Technical Interviews

Tools & Technologies Covered

• Docker

• Docker Compose

• AWS

• CloudWatch

• GitHub

Expected Outcomes

Students will be able to:

• Deploy AI applications successfully

• Utilize cloud infrastructure effectively

• Monitor production AI systems

• Create professional portfolios

• Prepare confidently for AI engineering interviews

Capstone Projects

Students will develop industry-oriented projects such as:

• AI Resume Screening System

• AI Interview Assistant

• Enterprise Knowledge Assistant

• AI Customer Support Agent

• AI Research Assistant

• AI Coding Assistant

• AI Voice Assistant

• Multi-Agent Business Automation Platform

• Internal Company Knowledge Chatbot

• Intelligent Document Processing System

Tools & Technologies Covered Across the Program

• Python

• VS Code

• Git & GitHub

• SQL

• Pytest

• GitHub Actions

• FastAPI

• OpenAI

• Gemini

• Hugging Face

• Ollama

• OpenWebUI

• LangChain

• LangGraph

• Vector Databases

• Redis

• Neo4j

• Mem0

• MCP

• Docker

• Docker Compose

• AWS

• CloudWatch

Career Opportunities

Upon successful completion of this program, learners can pursue roles such as:

• AI Engineer

• GenAI Developer

• Agentic AI Developer

• Python Developer

• RAG Engineer

• LLM Application Developer

• AI Automation Engineer

• Conversational AI Developer

• AI Solutions Engineer

• AI Product Engineer

Expected Outcomes of the Program

By the end of this program, learners will be able to:

• Develop robust applications using Python

• Build APIs and backend services using FastAPI

• Apply machine learning and deep learning concepts

• Work with OpenAI, Gemini, and Open Source LLMs

• Design and optimize prompts for AI systems

• Build Retrieval Augmented Generation (RAG) solutions

• Develop intelligent AI agents and multi-agent systems

• Implement memory-enabled and graph-based AI architectures

• Build conversational and voice-based AI applications

• Fine-tune and optimize language models

• Deploy production-ready AI solutions on cloud platforms

• Build a professional GitHub portfolio

• Prepare confidently for AI Engineering and Generative AI careers

• Successfully participate in placement drives and technical interviews