Industry Skills Classroom Training

LLM Developer

This course enables you will walk out not just with knowledge — but with the confidence, skills, and real project experience to land an LLM Developer role. You will be fully prepared to build intelligent LLM applications, design RAG pipelines, deploy production-ready AI APIs, and monitor them in real company environments — ready to perform from Day 1 on the job.

5.0 (6 ratings)
14 enrolled 6 Months | 4 hours/day Professional Tamil

What You'll Learn

  • Master Python & Prompt Engineering for LLM Development
  • Build Intelligent Applications with LangChain & LlamaIndex
  • Design & Deploy RAG Systems for Real Business Problems
  • Create Autonomous AI Agents with Tool Use & Memory
  • Deploy & Monitor LLM Applications in Production
  • Go from Job Seeker to Job-Ready LLM Developer

Requirements

  • Basic Computer Knowledge
  • No need of previous programming skills
  • Suitable for both IT & Non-IT Students

Tools & Technologies

Python Python
Hugging Face Hugging Face
Ollama Ollama
Langchain Langchain
LlamaIndex LlamaIndex
ChromaDB ChromaDB
Streamlit Streamlit
Fast API Fast API
PostgreSQL PostgreSQL
Phoenix Phoenix
Docker Docker
GitHub GitHub

Course Description

A professionally designed, industry-aligned classroom program built exclusively for job seekers who are serious about breaking into the world of AI and Large Language Models. This course trains you to build real-world LLM-powered applications — from intelligent chatbots and document Q&A systems to AI agents and production-ready APIs — giving you the confidence to walk into any company and deliver from your very first day at work.

Right for You?

Who This Course is For

  • Fresh Graduates Looking to Start Their Career in AI & LLM Development
  • Job Seekers Who Want to Build Real AI Applications Beyond Basic Python
  • Candidates Who Prefer Structured Classroom Learning with Mentor Support
  • Anyone Ready to Commit and Become a Job-Ready LLM Developer

Course Curriculum

16 modules • 152 lessons
Module 1: Python for LLM Developer 13 lessons
  • Introduction to Python & Environment Setup
  • Python Syntax & Data Types
  • Variables, Operators & Expressions
  • Conditional Statements & Loops
  • Functions & Modules
  • Lists, Tuples, Sets & Dictionaries
  • File Handling & Exception Handling
  • Object Oriented Programming
  • Working with JSON & YAML Files
  • Virtual Environments & Package Management
  • Async Programming Basics for API Calls
  • Python Interview Preparation
  • Module 1 Clearance Test
Module 2: NLP Fundamentals 10 lessons
  • Introduction to Natural Language Processing
  • Text Preprocessing & Cleaning
  • Tokenization - Word, Sentence & Subword
  • Stemming & Lemmatization
  • Bag of Words & TF-IDF
  • Word Embeddings - Word2Vec & GloVe
  • Sentence Embeddings & Semantic Similarity
  • Text Classification & Sentiment Analysis
  • Named Entity Recognition (NER)
  • Module 2 Clearance Test
Module 3: Large Language Models - Foundation 13 lessons
  • Introduction to Large Language Models
  • Transformer Architecture - Conceptual Overview
  • How LLMs are Trained - Pre-training & RLHF Concepts
  • GPT vs BERT vs T5 - Understanding Model Types
  • Context Windows, Temperature & Model Parameters
  • Open Source LLM Landscape - Llama, Mistral, Phi & Gemma
  • Introduction to Ollama - Running LLMs Locally
  • Setting Up & Running Models with Ollama
  • Introduction to Hugging Face Hub & Model Repository
  • Loading & Running Models from Hugging Face
  • Choosing the Right LLM for the Right Task
  • Hallucination, Bias & LLM Limitations
  • Module 3 Clearance Test
Module 4: Prompt Engineering 12 lessons
  • Introduction to Prompt Engineering
  • Zero Shot & Few Shot Prompting
  • System Prompts & Role-Based Prompting
  • Chain of Thought Prompting
  • Tree of Thought Prompting
  • Structured Output Control - JSON & XML Outputs
  • Context Window Management Strategies
  • Handling Hallucinations with Prompt Design
  • Prompt Templates & Reusability
  • Prompt Injection & Security Awareness
  • Advanced Prompt Optimization Techniques
  • Module 4 Clearance Test
Module 5: Working with LLM APIs & Local Models 10 lessons
  • Introduction to LLM APIs & Local Model Serving
  • Running LLMs Locally with Ollama API
  • Streaming Responses from Local LLMs
  • Using Hugging Face Inference API
  • Using Open Source Models via Hugging Face Pipelines
  • Function Calling with Open Source Models
  • Managing Model Parameters & Token Usage
  • Switching Between Different LLM Providers
  • Cost Optimization Strategies for LLM Applications
  • Module 5 Clearance Test
Module 6: LangChain for LLM Applications 12 lessons
  • Introduction to LangChain & Its Architecture
  • LangChain Components - Models, Prompts & Chains
  • Integrating Ollama & Hugging Face with LangChain
  • Building Simple Chains with LangChain
  • LangChain Expression Language (LCEL)
  • Memory & Conversation History in LangChain
  • LangChain Document Loaders & Text Splitters
  • LangChain Retrievers & Vector Store Integration
  • LangChain Tools & Tool Binding
  • Building Multi-Step Pipelines with LangChain
  • LangChain Output Parsers & Structured Responses
  • Module 6 Clearance Test
Module 7: Vector Databases & Embeddings 11 lessons
  • Introduction to Vector Databases & Why They Matter
  • Understanding Embeddings & Vector Representations
  • Embedding Models from Hugging Face & Ollama
  • Introduction to ChromaDB
  • Setting Up & Working with ChromaDB
  • Introduction to FAISS
  • Similarity Search & Semantic Search with FAISS
  • Indexing & Querying Vector Stores
  • Metadata Filtering in Vector Databases
  • Choosing the Right Vector Database for Your Use Case
  • Module 7 Clearance Test
Module 8: RAG - Retrieval Augmented Generation 11 lessons
  • Introduction to RAG & Why It Solves Hallucination
  • RAG Architecture - Indexing, Retrieval & Generation
  • Building a Basic RAG Pipeline with Ollama & ChromaDB
  • Document Loading & Chunking Strategies
  • Advanced Retrieval Techniques - Hybrid Search & Reranking
  • Introduction to LlamaIndex
  • Building RAG Pipelines with LlamaIndex
  • Building a Document Q&A Application
  • Building a Knowledge Base Application
  • RAG Evaluation & Performance Tuning
  • Module 8 Clearance Test
Module 9: LLM Agents & Tool Use 10 lessons
  • Introduction to LLM Agents & Agentic AI
  • ReAct Framework - Reasoning & Acting
  • Building Simple Agents with LangChain & Ollama
  • Function Calling & Tool Integration
  • Building Custom Tools for Agents
  • Agent Memory & State Management
  • Multi-Agent Systems - Concepts & Use Cases
  • Building a Multi-Agent Pipeline
  • Handling Agent Errors & Fallbacks
  • Module 9 Clearance Test
Module 10: Building LLM Applications with Streamlit 8 lessons
  • Introduction to Streamlit for LLM Apps
  • Building a Chat Interface with Streamlit & Ollama
  • Managing Conversation History in Streamlit
  • Building a Document Q&A App with Streamlit & RAG
  • Building a Multi-Tool LLM Assistant
  • Adding File Upload & Processing to LLM Apps
  • Deploying Streamlit LLM Applications
  • Module 10 Clearance Test
Module 11: LLM API Deployment with FastAPI 8 lessons
  • Introduction to FastAPI for LLM Deployment
  • Building a REST API for LLM Applications
  • Streaming LLM Responses via FastAPI
  • Integrating LangChain Pipelines into FastAPI
  • Authentication & API Key Management in FastAPI
  • Testing & Documenting FastAPI Endpoints
  • Containerizing LLM APIs with Docker
  • Module 11 Clearance Test
Module 12: SQL & Structured Data with LLMs 8 lessons
  • Introduction to Text-to-SQL with LLMs
  • Setting Up PostgreSQL for LLM Integration
  • Building a Text-to-SQL Pipeline with LangChain & Ollama
  • Querying Databases with Natural Language
  • Handling SQL Errors & Query Validation
  • Building a Natural Language Analytics Assistant
  • Structured Data Extraction from Unstructured Text
  • Module 12 Clearance Test
Module 13: LLM Evaluation & Safety 8 lessons
  • Introduction to LLM Evaluation
  • Evaluation Metrics - BLEU, ROUGE & BERTScore
  • RAG Evaluation - Faithfulness, Relevance & Completeness
  • Hallucination Detection & Mitigation
  • Bias & Fairness in LLM Outputs
  • Content Moderation & Output Filtering
  • Responsible AI Practices for LLM Developers
  • Module 13 Clearance Test
Module 14: LLM Observability & Monitoring 8 lessons
  • Introduction to LLM Observability
  • Tracking Token Usage & Model Performance
  • Monitoring Latency & Response Quality
  • Introduction to Phoenix by Arize
  • Tracing & Debugging LLM Pipelines with Phoenix
  • Setting Up Alerts & Performance Dashboards
  • Production Monitoring Best Practices for LLM Apps
  • Module 14 Clearance Test
Module 15: Version Control & LLM Project Structure 8 lessons
  • Introduction to Git & GitHub
  • Git Basics - Clone, Commit, Push & Pull
  • Branching, Merging & Pull Requests
  • LLM Project Folder Structure Best Practices
  • Managing API Keys & Secrets Safely with .env
  • Writing README & Documenting LLM Projects
  • Building a GitHub Portfolio for LLM Development
  • Module 15 Clearance Test
Module 16: Projects 2 lessons
  • Mentored Project
  • Capstone Project
Our Advantage

Why Choose This Course

  • Built Exclusively for Job Seekers
  • Most Practical LLM Course Available
  • Classroom Training with Real Mentor Guidance
  • Build an AI Portfolio That Gets You Hired

Your Instructor

Parthiban Kannan

Parthiban Kannan

Co-founder & Manager

17 Courses

14+ years of experience in digital transformation, leading teams of 15+ members and delivering 160+ projects for organizations like Lakshmi Machine Works, Milacron, Schneider Electric, Aatomz Research, and Variablz Technologies. AI innovator with a patent approved by the Government of India, with strong expertise in Data Science, Data Analysis, Business Intelligence, and Software Development.

FAQ

Frequently Asked Questions

Is this course suitable for freshers with no AI experience?

Yes. This course starts from Python and NLP basics and gradually takes you to advanced LLM application development u2014 no prior AI experience is required to join.

What are the eligibility criteria to join this course?

Any graduate from any degree is eligible, provided their degree includes Mathematics as a subject.

Is this course suitable for college students or working professionals?

No. This course is exclusively designed for job seekers who are seriously looking to start their career as an LLM Developer.

Is this an online or offline course?

This is a completely classroom-based training program conducted at our Cuddalore location.

Will I work on real projects during this course?

Yes. You will work on a Mentored Project with trainer guidance and an independent Capstone Project u2014 both built around real-world LLM application scenarios.

What is the difference between an LLM Developer and an LLM Engineer?

An LLM Developer focuses on building applications using existing LLMs u2014 chatbots, RAG systems, agents, and APIs. An LLM Engineer goes deeper into the model itself u2014 fine tuning, training pipelines, and model optimization. This course trains you as an LLM Developer.

Do I need to pay for LLM APIs like OpenAI during the course?

OpenAI provides free credits for new accounts and Hugging Face offers free open source models. We will guide you on how to use free tiers and cost optimization strategies so you can practice without significant expense.

What kind of companies can I apply to after this course?

You will be job-ready for AI startups, product companies, MNCs, and service companies across Tamil Nadu and South India hiring for LLM Developer, AI Developer, Prompt Engineer, and Generative AI Developer roles.

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