Industry Skills Classroom Training

LLM Engineer

This course enables you to walk out not just with knowledge — but with the confidence, skills, and real project experience to land an LLM Engineer role. You will be fully prepared to fine tune foundation models, build RAG pipelines, serve models at scale, track experiments, and deliver production-ready LLM systems — ready to perform from Day 1 on the job.

5.0 (3 ratings)
12 enrolled 6 Months | 4 hours/day Professional Tamil

What You'll Learn

  • Master Python, PyTorch & Transformer Architecture for LLM Engineering
  • Fine Tune Foundation Models with LoRA, QLoRA & PEFT using Google Colab
  • Build & Evaluate RAG Systems and LLM Agents
  • Serve & Optimize LLMs for Production with vLLM & Ollama
  • Track & Monitor LLM Experiments with MLflow & Phoenix
  • Go from Job Seeker to Job-Ready LLM Engineer

Requirements

  • Basic Computer Knowledge
  • No need of previous coding knowledge
  • Suitable for both IT & Non-IT students

Tools & Technologies

Python Python
PyTorch PyTorch
Ollama Ollama
Hugging Face Hugging Face
Langchain Langchain
LlamaIndex LlamaIndex
ChromaDB ChromaDB
vLLM vLLM
Streamlit Streamlit
Fast API Fast API
MLflow MLflow
Phoenix Phoenix
PostgreSQL PostgreSQL
Docker Docker
GitHub GitHub
Google Colab Google Colab

Course Description

A professionally designed, industry-aligned classroom program built exclusively for job seekers who are serious about becoming an LLM Engineer. This course goes beyond building applications — it trains you to work closer to the model itself, fine tuning foundation models, optimizing them for production, and serving them at scale — giving you the confidence to walk into any AI-first 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 Engineering
  • Job Seekers Who Want to Go Beyond App Development and Work Closer to the Model
  • Candidates Who Prefer Structured Classroom Learning with Mentor Support
  • Anyone Ready to Commit and Become a Job-Ready LLM Engineer

Course Curriculum

18 modules • 174 lessons
Module 1: Python for LLM Engineer 14 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 & Concurrency
  • Python for Data Handling & Preprocessing
  • Python Interview Preparation
  • Module 1 Clearance Test
Module 2: NLP Fundamentals & Text Processing 11 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)
  • Text Data Augmentation Techniques
  • Module 2 Clearance Test
Module 3: Deep Learning & Transformer Architecture 12 lessons
  • Introduction to Deep Learning for LLMs
  • Neural Networks - Perceptron & Multilayer Networks
  • Activation Functions, Loss Functions & Optimizers
  • Backpropagation & Gradient Descent
  • Introduction to PyTorch
  • PyTorch Tensors & Operations
  • Building Neural Networks with PyTorch
  • Attention Mechanism - Self Attention & Multi-Head Attention
  • Transformer Architecture - Encoder & Decoder
  • Positional Encoding & Layer Normalization
  • BERT, GPT & T5 Architecture Deep Dive
  • Module 3 Clearance Test
Module 4: Large Language Models - Foundation to Advanced 12 lessons
  • Introduction to Large Language Models
  • LLM Pre-training - Concepts & Data Requirements
  • Instruction Tuning & RLHF Concepts
  • Context Windows, Temperature & Sampling Strategies
  • Open Source LLM Landscape - Llama, Mistral, Phi & Gemma
  • Setting Up & Running Models with Ollama
  • Loading & Running Models from Hugging Face
  • Model Quantization - GGUF, GPTQ & AWQ
  • Running Quantized Models Efficiently
  • Choosing the Right LLM for Production Use Cases
  • Hallucination, Bias & LLM Limitations
  • Module 4 Clearance Test
Module 5: Prompt Engineering & Advanced Techniques 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 5 Clearance Test
Module 6: Working with LLM APIs & Local Models 9 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
  • Module 6 Clearance Test
Module 7: Vector Databases & Embeddings 10 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
  • Module 7 Clearance Test
Module 8: RAG - Retrieval Augmented Generation 10 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
  • 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: Dataset Preparation for Fine Tuning 10 lessons
  • Introduction to Fine Tuning & When to Use It
  • Types of Fine Tuning - Instruction, Task & Domain Specific
  • Dataset Formats for Fine Tuning - Alpaca, ShareGPT & Chat Format
  • Collecting & Curating Training Data
  • Data Cleaning & Quality Filtering for LLMs
  • Data Augmentation for LLM Training
  • Tokenizing & Formatting Datasets with Hugging Face
  • Splitting & Validating Fine Tuning Datasets
  • Pushing Datasets to Hugging Face Hub
  • Module 10 Clearance Test
Module 11: Fine Tuning Foundation Models - LoRA & PEFT 12 lessons
  • Introduction to Parameter Efficient Fine Tuning (PEFT)
  • LoRA - Low Rank Adaptation Concepts
  • QLoRA - Quantized LoRA for Resource Efficient Fine Tuning
  • Setting Up Google Colab with T4 GPU for Fine Tuning
  • Setting Up Fine Tuning Environment with Hugging Face & PEFT
  • Fine Tuning Llama & Mistral with LoRA
  • Fine Tuning with TRL - Supervised Fine Tuning Trainer
  • Instruction Fine Tuning on Custom Datasets
  • Merging LoRA Adapters with Base Model
  • Saving & Loading Fine Tuned Models
  • Pushing Fine Tuned Models to Hugging Face Hub
  • Module 11 Clearance Test
Module 12: Model Evaluation & Benchmarking 10 lessons
  • Introduction to LLM Evaluation
  • Evaluation Metrics - BLEU, ROUGE & BERTScore
  • Perplexity & Loss Evaluation
  • Task Specific Evaluation - Classification, QA & Summarization
  • RAG Evaluation - Faithfulness, Relevance & Completeness
  • Hallucination Detection & Mitigation
  • Benchmarking Fine Tuned vs Base Models
  • Human Evaluation Strategies
  • Bias & Safety Evaluation
  • Module 12 Clearance Test
Module 13: Model Serving & Optimization 9 lessons
  • Introduction to Model Serving for Production
  • Model Quantization for Efficient Serving
  • Introduction to vLLM for High Performance Serving
  • Setting Up & Running Models with vLLM
  • Batching & Throughput Optimization with vLLM
  • Serving Fine Tuned Models with Ollama
  • Latency & Memory Optimization Techniques
  • Containerizing Model Servers with Docker
  • Module 13 Clearance Test
Module 14: 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 vLLM & Ollama with FastAPI
  • Authentication & API Key Management in FastAPI
  • Testing & Documenting FastAPI Endpoints
  • Containerizing LLM APIs with Docker
  • Module 14 Clearance Test
Module 15: 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 Applications
  • Module 15 Clearance Test
Module 16: MLflow for LLM Experiment Tracking 7 lessons
  • Introduction to MLflow & Experiment Tracking
  • Setting Up MLflow for LLM Projects
  • Logging Fine Tuning Experiments with MLflow
  • Tracking Hyperparameters, Metrics & Artifacts
  • Comparing Fine Tuning Runs & Model Versions
  • MLflow Model Registry & Versioning
  • Module 16 Clearance Test
Module 17: Version Control & LLM Project Structure 8 lessons
  • Introduction to Git & GitHub
  • Git Basics - Clone, Commit, Push & Pull
  • Branching, Merging & Pull Requests
  • LLM Engineer Project Folder Structure Best Practices
  • Managing API Keys & Secrets Safely with .env
  • Writing README & Documenting LLM Engineering Projects
  • Building a GitHub Portfolio for LLM Engineering
  • Module 17 Clearance Test
Module 18: Projects 2 lessons
  • Mentored Project
  • Capstone Project
Our Advantage

Why Choose This Course

  • Built Exclusively for Job Seekers
  • Only Course That Covers Fine Tuning in a Classroom
  • Classroom Training with Real Mentor Guidance
  • Build a Portfolio That Puts You in the Top 1%

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 deep learning experience?

Yes. This course covers Deep Learning and Transformer Architecture from scratch in Module 3 u2014 no prior deep learning 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. Strong mathematical foundation helps especially for understanding transformer architecture and fine tuning concepts.

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 Engineer.

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 engineering scenarios including fine tuning and model deployment.

What is the difference between this course and the LLM Developer course?

The LLM Developer course focuses on building applications using existing LLMs u2014 chatbots, RAG systems, and APIs. The LLM Engineer course goes deeper u2014 covering transformer architecture, fine tuning foundation models with LoRA and PEFT, model serving with vLLM, and experiment tracking with MLflow.

Do I need a GPU for fine tuning during the course?

No personal GPU is required. For all fine tuning modules, we use Google Colab's free T4 GPU u2014 completely free, no setup cost, no hardware purchase needed. Students will learn to run fine tuning jobs on Colab which is also a real-world skill used by professionals globally.

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

You will be job-ready for AI-first companies, product startups, MNCs, and research-oriented firms across Tamil Nadu and South India hiring for LLM Engineer, AI Engineer, ML Engineer, and Generative AI Engineer roles.

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