AI Engineering Course - Generative AI & Agentic AI

Q: What is an AI Engineering course?

An AI Engineering course teaches you how to build, customize, evaluate, and deploy AI-powered applications. This course covers Neural Networks, Transformers, Hugging Face, Generative AI, LLMs, Prompt Engineering, RAG, Vector Databases, Agentic AI, MCP, LangGraph, LLM Evaluation, Observability, Docker, and deployment.

Q: How to become an AI Engineer?

To become an AI Engineer, you need a combination of Python, AI/ML fundamentals, LLMs, Generative AI, RAG, AI agents, model customization, evaluation, and deployment skills. This course follows that progression, starting with Neural Networks and Transformers and moving toward RAG, Agentic AI, MCP, evaluation, and production deployment.

Q: What are the prerequisites for an AI Engineering course?

The primary prerequisite for this course is basic Python knowledge. You do not need prior experience with LLMs, RAG, Agentic AI, or Generative AI. The curriculum starts with Neural Network fundamentals and progressively introduces advanced AI engineering concepts.

Q: What will I learn in an AI Engineering course?

You will learn Neural Networks, Transformers, Hugging Face, Transfer Learning, QLoRA, Generative AI, Prompt Engineering, LangChain, Embeddings, Vector Databases, RAG, Local LLMs, Agentic AI, MCP, LangGraph, LLM Evaluation, Observability, Docker, and AI application deployment.

Q: Will I learn Generative AI and LLMs in this course?

Yes. The course covers Generative AI fundamentals, Foundation Models, LLM interactions, Prompt Engineering, Hugging Face models, local LLMs, RAG, Agentic AI, evaluation, and deployment. The curriculum focuses on using these technologies to build practical AI applications.

Q: Will I learn Transformers and Attention mechanisms?

Yes. You will learn Seq2Seq models, Attention, Self-Attention, Attention Score calculation, Multi-Head Attention, Positional Encoding, Encoders, Decoders, Residual Connections, Layer Normalization, and the Linear and Softmax layers used in Transformer architectures.

Q: Will I learn Hugging Face and LLM fine-tuning?

Yes. The course covers the Hugging Face Models Hub, Inference API, Pipelines, AutoTokenizer, AutoModel, Datasets, and Transfer Learning. You will also learn Training APIs, LoRA, Quantization, QLoRA concepts, and Supervised Fine-Tuning.

Q: What is RAG and will I learn how to build RAG applications?

Yes. Retrieval Augmented Generation (RAG) combines retrieved information with an LLM to generate more context-aware responses. You will learn chunking, document loaders, text splitters, embeddings, vector stores, retrievers, RAG architecture, and practical RAG applications.

Q: What advanced RAG techniques will I learn?

The course goes beyond basic RAG and covers Re-ranking, Query Decomposition, HyDE, Agentic RAG, Hybrid Search, Metadata Filtering, and Context Compression. These techniques help you understand how retrieval quality, context, and LLM responses can be improved.

Q: Will I learn Vector Databases and Embeddings?

Yes. You will learn how embeddings represent information as vectors and how vector databases enable semantic search. The curriculum covers similarity metrics, types of search, vector database concepts, and their application in RAG and modern LLM-based systems.

Live Course

120k+ interested Geeks

This 12-week course takes you from neural network fundamentals to building production-ready AI applications. Learn Transformers, prompt engineering, RAG, vector databases, Agentic AI, LLM evaluation, and deployment through live, mentor-led sessions and 13 hands-on projects using Hugging Face, LangChain, CrewAI and LangGraph. Earn dual IBM & GeeksforGeeks certification.

Intermediate and Advanced

12 Weeks

2 Seats Left

Hurry! Batch Closing This Saturday — Enroll Now! Need guidance? Call +91-9217990450

Course Overview

IBM & GeeksforGeeks Certification

Earn industry-recognized certification

Modern AI Toolkit

Work with the latest AI tools and technologies

Live Soft Skills Sessions

Biweekly expert-led sessions for interview success

Skills You'll Gain

Key AI Engineering concepts you'll learn and master.

Prompt Engineering

Retrieval-Augmented Generation

Intelligent Document Processing

Agentic AI Design

Multi-Agent Orchestration

LLM Evaluation and Observability

Containerization

LLM Deployment Patterns

Tech Stack You'll Learn

Tools and technologies you'll work with

Hugging Face

LangChain

Ollama

CrewAI

LangGraph

Google ADK

OpenAI Agents SDK

MCP and Agent Skills

DeepEval

LangSmith

Docker

Gradio

Projects You'll Build

Real-world projects to apply your skills

MNIST Handwritten Digit Classification

Build your first end-to-end neural network by training a model to recognize handwritten digits and understand how forward propagation and backpropagation work in a practical deep learning workflow.

PythonKerasMNIST

Hugging Face Pipeline Applications

Build multiple mini-projects using Hugging Face pipelines to explore pre-trained AI models and understand how modern NLP and AI tasks can be integrated into applications.

Hugging FaceTransformersPipeline API

Custom Domain Model with Transfer Learning

Adapt a pre-trained open-source model to a custom domain using transfer learning and the Hugging Face Trainer API, gaining practical experience in model adaptation and fine-tuning.

Hugging Face TransformersTrainer APIPyTorch

AI-Powered Data Analysis with Prompt Engineering

Build an AI-powered data analysis workflow that uses large language models to interact with and analyze real-world datasets through effective prompt engineering.

Prompt EngineeringGeminiNotebookLM

Text2SQL: Natural Language Database Querying

Create a Natural Language to SQL system that enables non-technical users to query databases using plain English and convert natural-language questions into executable SQL queries.

Prompt EngineeringGoogle Gen AI SDKSQLite

AI Review Chatbot with Gradio

Develop a grounded AI chatbot that answers questions about review data and deploy a shareable interactive interface using Gradio.

EmbeddingsGradioRAG

Prompt Engineering with Local Small Language Models

Build AI workflows using small language models running locally, exploring how prompt engineering can improve model behavior without relying entirely on cloud-based APIs.

OllamaGemma 4Prompt Engineering

Offline RAG with Local SLMs

Build a fully local Retrieval-Augmented Generation pipeline using small language models, enabling private document retrieval without sending data to external APIs.

OllamaChromaQwen3-Embedding

AI Market Research Crew

Build an Agentic AI system where multiple specialized AI agents collaborate to research a market, analyze information, and generate a structured research report.

CrewAILLMsMulti-Agent Systems

AI Background Verification & Due Diligence

Develop an AI agent system that uses tools, memory, and a knowledge base to perform automated background verification and support real-world due-diligence workflows.

CrewAIMCP ServersAgent Skills

Multi-Agent AI Fact-Checking System

Build a multi-agent fact-checking system with an orchestrator, specialized agents, and adversarial debate to analyze claims and improve the reliability of AI-generated conclusions.

n8nLow-Code Agentic AIMCP Servers

Fact-Checking System with LangGraph

Rebuild the fact-checking workflow using LangGraph to implement graph-based agent orchestration, routing, cycles, and checkpoint-based execution.

LangGraphLow-Code Agentic AIMCP Servers

CAPSTONE

End-to-End AI Engineering Capstone

Build your own production-oriented AI application from ideation to deployment. Containerize the application, deploy it, and receive one-on-one review from industry professionals to strengthen your portfolio.

DockerCloud DeploymentGitHub Actions

Course Syllabus

AI Engineering Course - Generative AI & Agentic AI

AI Toolkit for Developers

Prerequisite

Basic knowledge of Python

Module 1: Feedforward Neural Networks

Feedforward Neural Networks

Understanding the AI Glossary
Perceptron and Activation Functions
Layers and MLPs
Understanding the Training Loop

Backpropagation

Backpropagation by Hand
Project: MNIST Handwritten Digit Classification

Module 2: Transformers – Concepts

Transformers - Conceptual Knowledge

Seq2Seq Models with RNN
What is Attention?
A High-Level Look at Transformers
Encoders
Self-Attention

Transformers - Conceptual Knowledge

How are attention scores calculated?
Multi-Head Attention
Positional Encoding
Residuals & Layer Normalization
Decoders
Linear and Softmax Layers
Revisiting the High-Level Look

Module 3: Hugging Face & Transfer Learning

Hugging Face Basics

What is Hugging Face?
Hugging Face Model Hub Web Interface
Understanding Model Cards
The Inference API
The Pipeline Abstraction
The AutoTokenizer Family
Tokenization Strategies
The AutoModel Family
Hugging Face Datasets
Project: Multiple Mini-Projects Using the Pipeline Abstraction

Transfer Learning with Hugging Face and QLoRA

What is Transfer Learning?
The TrainingArguments Class
The Trainer API
Project: Transfer Learning Project
Low-Rank Adaptation
Quantization
Supervised Fine-tuning

Upcoming Batches

Batch

Mentor

STARTING FROM

TIMINGS

AI Engineering Course - Generative AI & Agentic AI

Course Overview

IBM & GeeksforGeeks Certification

Modern AI Toolkit

Live Soft Skills Sessions

Skills You'll Gain

Tech Stack You'll Learn

Projects You'll Build

Course Syllabus

Prerequisite

Module 1: Feedforward Neural Networks

Module 2: Transformers – Concepts

Module 3: Hugging Face & Transfer Learning

1: The AI Toolkit & Developer Setup

2: Prompt Engineering & Vibe Coding

3: Spec-Driven Development with AI

4: RAG Systems & Context-Aware AI

Upcoming Batches

Frequently Asked Questions

What is an AI Engineering course?

How to become an AI Engineer?

What are the prerequisites for an AI Engineering course?

What will I learn in an AI Engineering course?

Will I learn Generative AI and LLMs in this course?

Will I learn Transformers and Attention mechanisms?

Will I learn Hugging Face and LLM fine-tuning?

What is RAG and will I learn how to build RAG applications?

What advanced RAG techniques will I learn?

Will I learn Vector Databases and Embeddings?

Will I learn Prompt Engineering and LangChain?

Will I learn Agentic AI and AI Agents?

Which Agentic AI frameworks will I learn?

Will I learn MCP and how to connect tools to AI Agents?

Can I run LLMs locally with this course?

Will I learn how to evaluate and monitor LLM applications?

Will I learn how to deploy AI and LLM applications?

What projects will I build in this AI Engineering course?

What jobs can I get after an AI Engineering course?

What salary can I expect after learning AI Engineering?

Is an AI Engineering course useful for 1st, 2nd, 3rd, and final-year students?

Is an AI Engineering course useful for BCA, MCA, and M.Tech students?

How much time does it take to complete an AI Engineering course?

Is AI Engineering different from Data Science and Machine Learning?

Do I need to learn DSA, DBMS, OS, CN, and System Design for AI Engineering?

How is this AI Engineering course different from other AI courses?

Is this AI Engineering course enough to become job-ready?

What are the key features of this AI Engineering course?

Is there a refund option if I am not satisfied with the course?

Is there a contact number for queries?