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LLM Engineering Training Course in Chennai by Uncodemy

Chennai learners can access Uncodemy's LLM Engineering program as a 5–6 month instructor-led live online course. The syllabus walks through LLM APIs, embeddings & vector databases, RAG, fine-tuning, LLM agents & tool calling, and deployment. Geared toward developers, data scientists/ML engineers and aspiring AI engineers, it consists of 13 modules, 100+ live sessions, 160+ hours and an enterprise LLM capstone. Fee: ₹17,500 + 18% GST, total ₹20,650. EMI from ₹4,032/month.

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Starting ₹4,032/M*

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Expert Trainers

Learn from 8+ years experienced professionals

Hands-on Automation Projects

Build real LLM test suites, not just theory

Lifetime Access

Get lifetime access to recorded sessions & materials

Dedicated Career & Placement Assistance

Resume Building, Mock Interviews & Job Support

Certified Course

Certificate of Completion from Uncodemy

Flexible Batches

Weekday & Weekend Online & Offline

Why Choose
Uncodemy?

14+

Years of Training Excellence

850+

Companies in Our Hiring Network

54,000+

Learners Trained

200+

Corporate Associations

100%

Placement Support

Online & Offline Classes

Weekend & Weekday Batches

100% Practical Training

Affordable Fees

EMI Options Available

Dedicated Support

Program Details

LLM Engineering Course — Live Online Program Hands-on learning that focuses on practical LLM engineering skills and interview preparation.

A comprehensive 5–6 month instructor-led live online program covering LLM APIs, embeddings & vector databases, RAG, fine-tuning, LLM agents & tool calling, and deployment along with practical projects, mentoring and career support.

📚 Learning:

160+ Hours of Learning

⏱️ Program Duration:

5–6 Months

🎓 Live Training:

100+ Live Sessions

🛠️ Technology Coverage:

15+ Tools & Technologies

💼 Practical Learning:

Hands-on Projects, RAG Applications & AI Case Studies

💰 Program Fee:

₹17,500 + 18% GST

💰 Total Fee:

₹20,650

💳 EMI:

Starting From ₹4,032/Month*

🚀 Career Support:

Dedicated Career & Placement Assistance

🏢 Hiring Network:

850+ Companies in Our Hiring Network

🎯 Career Support Includes:

Resume Building & LinkedIn Optimization, Mock Interviews & Interview Preparation, Career Guidance & Job Opportunity Assistance

LLM Engineering Course in Chennai — Quick Facts

🎓 Course:

LLM Engineering

🏢 Provider:

Uncodemy Edutech Pvt. Ltd.

📚 Total Learning:

160+ Hours of Learning

📖 Curriculum:

13 Modules

💼 Projects:

Hands-on Projects + Enterprise LLM Capstone

🛠️ Tools:

LLM APIs/SDKs, LangChain, Vector DBs, Python, Hugging Face, Prompt Engineering, Fine-Tuning, Evaluation, Git

🌐 Modes:

Live Online (offline only at Delhi & Noida centres; Chennai learners join live online)

✅ Prior Coding:

Programming fundamentals helpful; Python taught from Module 2 onward. Basic ML/NLP built up in Module 3.

📜 Certificate:

Uncodemy LLM Engineering Program Certificate (verifiable online)

🗓️ Last Reviewed:

3 October 2026

List of Placed Students Trained by Uncodemy

Those who complete Uncodemy's LLM Engineering program build practical, job-ready expertise across LLM APIs, RAG pipelines, fine-tuning, vector databases and LLM agents. Once training and career preparation are done, eligible learners can apply for roles such as LLM Engineer, AI Engineer and RAG/AI Application Engineer. Placement records for Chennai learners are refreshed periodically — see verified outcomes at uncodemy.com/placement.

What is LLM Engineering?

LLM engineering refers to designing, building, fine-tuning and deploying applications powered by large language models. Traditional software engineering relies on deterministic logic; data science focuses on predictive modelling from data. LLM engineering instead works with probabilistic models, prompt design, retrieval pipelines and model adaptation. It merges software engineering, machine learning and product development into one discipline, turning raw LLM capabilities into dependable, production-grade features.

What does an LLM engineer do? An LLM engineer works with LLM APIs and SDKs, builds RAG pipelines with embeddings and vector databases, fine-tunes models for domain-specific tasks, designs LLM agents with tool calling, evaluates model outputs, and deploys LLM-powered applications. Day-to-day work includes Python scripting, prompt engineering, retrieval evaluation, API integration, and using frameworks like LangChain to bring models, data and tools into one working application.

LLM Engineering vs Generative AI vs Data Science

LLM Engineering focuses on building, fine-tuning and deploying LLM-powered applications, while Generative AI & Agentic AI emphasises autonomous agents and Data Science focuses on predictive modelling.

Aspect	LLM Engineering	Generative AI	Data Science
Core focus	Building, fine-tuning & deploying LLM-powered applications	Building GenAI apps + autonomous AI agents/multi-agent systems	Predictive modelling from data
Coding depth	High	Moderate to High	High
Typical entry salary	₹6–10 LPA (Entry-Level LLM Engineer)	[CONFIRM per GenAI course sheet]	₹4–8 LPA (per Data Science course sheet)
Uncodemy program duration	5–6 months	~3–4 months (indicative)	8–9 months

Salary ranges are indicative and vary by role, skills, experience, location and employer.

Course Overview

Chennai learners can enrol in this LLM Engineering course as a fully live online program that runs 5–6 months, delivering 100+ live sessions and 160+ hours of learning. The 13 modules progress in a structured order — Generative AI foundations, Python for LLM engineering, ML & NLP fundamentals, transformer architecture, prompt engineering, LLM APIs & SDKs, embeddings & vector databases, RAG, LLM application development, fine-tuning, LLM agents & tool calling, evaluation & deployment, and a 20-hour Enterprise LLM capstone. Each module blends conceptual learning with hands-on practice using real tools, and the program wraps up with projects, RAG applications and an enterprise-grade capstone. Everything is delivered online, making it easy for Chennai and pan-India learners to join.

Who should join this course:

Developers who want to specialise in LLM-powered applications and generative AI.
Data scientists and ML engineers ready to upskill into LLM engineering and RAG pipelines.
Aspiring AI engineers looking to build production-ready LLM applications.
Working professionals shifting into GenAI/LLM roles from software or analytics backgrounds.
Chennai-based learners who want instructor-led live online training with real hands-on projects.

Eligibility: Programming basics are useful but not required — Python is covered from Module 2 onward. Familiarity with basic ML/NLP concepts is beneficial but developed in Module 3. A laptop with reliable internet is needed for practical sessions.

Upcoming LLM Engineering Course Batches for Chennai Learners

Pick a live online batch timing that suits your daily routine — every batch runs on IST. A free demo class is a great way to check the delivery style and curriculum before you decide. After that, choose from upcoming weekday or weekend batches, designed for both working professionals and students, with flexible timings and a strong hands-on focus.

Weekdays

Date	Time	Trainer	Seats Left
16oct	07:30 PM – 08:30 PM	Mr. Irshad	01Seat
22oct	02:00 PM – 03:00 PM	Mr. Upendra Kumar Tiwari	02Seats
26oct	10:30 AM – 11:30 AM	Mr. Irshad	01Seat
30oct	12:00 PM – 01:00 PM	Mr. Upendra Kumar Tiwari	02Seats

Weekends

Date	Time	Trainer	Seats Left
27oct	03:00 PM – 05:00 PM	Mr. Irshad	02Seats

8+ new batches start every month. All timings are in IST. Seats are limited and updated daily.

Enroll Now

Curriculum for LLM Engineering Training Course in Chennai

LLM Engineering Course Syllabus in Chennai

The syllabus is organised into 13 modules covering 160 hours. The order follows how LLM engineering actually works: foundations → Python → ML/NLP basics → transformer architecture → prompt engineering → LLM APIs → embeddings/vector DBs → RAG → application development → fine-tuning → agents & tool calling → evaluation/deployment → capstone. Each module includes practical exercises with real tools, and the program closes with hands-on projects, RAG applications and an enterprise LLM capstone.

Module 1: LLM & Generative AI Foundations — 8 Hours

Introduction to large language models and generative AI
LLM architecture basics: parameters, context windows
Tokenization, embeddings and model behaviour
GenAI landscape: major model providers and use cases
How LLMs differ from traditional software systems
Probabilistic outputs and non-determinism in LLMs

Module 2: Python for LLM Engineering — 10 Hours

Python syntax, data structures and control flow
Functions, modules and packages
Working with APIs, JSON and environment variables
Virtual environments and dependency management
Error handling and debugging for LLM applications
Async programming basics for API calls

Module 3: Machine Learning & NLP Fundamentals — 10 Hours

Supervised vs unsupervised learning basics
Training, validation and test splits
Text preprocessing and tokenization for NLP
Word embeddings and vector representations
Attention mechanisms and sequence modelling
Evaluation metrics for NLP tasks

Module 4: Transformers & LLM Architecture — 12 Hours

Transformer architecture: encoder, decoder, attention
Self-attention and multi-head attention
Positional encoding and context length
Pre-training vs fine-tuning paradigms
Model sizes, distillation and quantization
Hands-on: loading and inspecting transformer models

Module 5: Prompt Engineering & Structured Outputs — 8 Hours

Prompt design patterns and best practices
Zero-shot, few-shot and chain-of-thought prompting
Structured outputs: JSON, XML, function schemas
Prompt versioning and regression testing
Handling ambiguity and edge cases
Evaluating prompt reliability

Module 6: Working with LLM APIs & SDKs — 10 Hours

Overview of LLM APIs (OpenAI/Gemini/Claude-style)
Authentication, rate limits and error handling
Streaming responses and async calls
Function calling and structured output APIs
Cost management and token optimization
Building reusable API wrappers in Python

Module 7: Embeddings & Vector Databases — 10 Hours

Text embeddings and semantic similarity
Vector databases: FAISS, Chroma, Pinecone
Indexing strategies and performance trade-offs
Chunking strategies for documents
Metadata filtering and hybrid search
Hands-on: building a vector search pipeline

Module 8: Retrieval-Augmented Generation (RAG) — 15 Hours

RAG architecture: retriever, generator, context
Building end-to-end RAG pipelines with LangChain
Retrieval accuracy and relevance evaluation
Reranking and context compression
Handling multi-document and multi-hop queries
Hands-on: building a production-style RAG application

Module 9: LLM Application Development — 12 Hours

Designing LLM-powered applications
Chains, memory and context management
Integrating LLMs with external data sources
Building chat interfaces and APIs
Error handling and fallback strategies
Hands-on: building an LLM app with LangChain

Module 10: Fine-Tuning & Model Adaptation — 15 Hours

When to fine-tune vs prompt engineering vs RAG
Dataset preparation and formatting for fine-tuning
Parameter-efficient fine-tuning (LoRA, adapters)
Using Hugging Face Transformers for fine-tuning
Evaluating fine-tuned model performance
Hands-on: fine-tuning a small LLM on a custom dataset

Module 11: LLM Agents & Tool Calling — 15 Hours

Agent architecture: planning, tool use, memory
Tool calling and function execution
Multi-step reasoning and task decomposition
Building agents with LangChain
Agent evaluation and failure mode analysis
Hands-on: building a tool-using LLM agent

Module 12: LLM Evaluation, Optimization & Deployment — 15 Hours

Evaluation metrics for LLM outputs
Automated evaluation with LLM-as-judge
Observability and tracing for LLM applications
Latency, cost and throughput optimization
Deployment patterns: APIs, containers, serverless
Monitoring production LLM applications

Module 13: Enterprise LLM Projects & Capstone — 20 Hours

End-to-end LLM application design and architecture
RAG + fine-tuning + agents integration project
Deployment and evaluation of capstone application
Code review and portfolio preparation
Presentation and documentation of capstone project
Interview preparation around capstone work

#	Module	Hours	Tools
1	LLM & Generative AI Foundations	8	ChatGPT, Gemini, Claude
2	Python for LLM Engineering	10	Python, Git
3	Machine Learning & NLP Fundamentals	10	Python, scikit-learn, NLTK
4	Transformers & LLM Architecture	12	Hugging Face Transformers
5	Prompt Engineering & Structured Outputs	8	ChatGPT, Gemini, Claude
6	Working with LLM APIs & SDKs	10	OpenAI/Gemini/Claude APIs, Python
7	Embeddings & Vector Databases	10	FAISS, Chroma, Pinecone
8	Retrieval-Augmented Generation (RAG)	15	LangChain, Vector DBs
9	LLM Application Development	12	LangChain, Python
10	Fine-Tuning & Model Adaptation	15	Hugging Face, LoRA
11	LLM Agents & Tool Calling	15	LangChain, Python
12	LLM Evaluation, Optimization & Deployment	15	LangSmith, Docker, Python
13	Enterprise LLM Projects & Capstone	20	All tools
Total		160 Hours	15+ tools

Contacted by

Mr. Irshad

Curriculum & Technical Reviewer

[8+ Years Of Experience]

Specialization: LLM Engineering, Generative AI, RAG & Fine-Tuning

LinkedIn Profile

LLM Engineering Curriculum

The curriculum has been designed and technically reviewed by expert industry professional Mr. Irshad, with additional review by Mr. Upendra Kumar Tiwari.

160+Hours of Learning

100+Live Sessions

15+Tools & Technologies

Get Your LLM Engineering Certification In Chennai

Once all 13 modules, the assignments and the Enterprise LLM capstone project are complete, learners receive the Uncodemy LLM Engineering Program Certificate. The certificate captures the program name, duration, tools covered and completion date. It can be verified online at certificate.uncodemy.com and serves as a formal record of successful completion of the Uncodemy LLM Engineering program.

The certificate reflects the practical work completed during the program — hands-on RAG applications, fine-tuning exercises and the 20-hour Enterprise LLM capstone project. Learners work with LLM APIs & SDKs (OpenAI/Gemini/Claude-style), LangChain, Vector Databases (FAISS/Chroma/Pinecone), Python, Hugging Face Transformers, prompt engineering tools, fine-tuning frameworks and evaluation/observability tools.

Key Benefits of Our LLM Engineering Certification:

Program Completion :Provides a record of successful completion of the LLM Engineering program.
Online Verification :Certificate is verifiable online at certificate.uncodemy.com.
Practical Learning :Training includes hands-on RAG applications, fine-tuning work and an enterprise capstone.

This is an independent Uncodemy certificate. It is not affiliated with, endorsed by, or a credential of OpenAI, Google, Anthropic or any other LLM/vendor provider whose APIs are taught in this course. Certificate verification process pending confirmation.

Uncodemy Certificate Verification Online Certificate Verification & Download certificate.uncodemy.com

IIBA Endorsed Education Provider certificate — Uncodemy

Uncodemy LLM Engineering Program Certificate

Tools and Technologies Covered

LLM APIs & SDKs
LangChain
Vector Databases
Python
Hugging Face
Prompt Engineering
Fine-Tuning
Evaluation/Observability
Git & GitHub

Apply Now

Introducing Uncodemy

Uncodemy — a career-oriented IT training institute under Uncodemy Edutech Pvt. Ltd. — is backed by 14+ years of training excellence. It runs physical classroom centres in Delhi and Noida, plus live online classes for learners across India, delivering instructor-led training in emerging technologies.

Through this LLM Engineering program, learners develop RAG applications, fine-tune models, design LLM agents with tool calling, and deliver an enterprise LLM capstone project. The course addresses how LLM-powered applications are engineered in production — prompt design, retrieval pipelines, model adaptation and deployment. You can view the full module-by-module curriculum before you enrol.

Besides training, Uncodemy supplies instructor-led sessions, practical assignments, project-based learning, interview preparation and career support. What that support includes — and what it does not — is set out in our career support policy, along with the eligibility conditions published before enrolment rather than after payment.

Other courses for Chennai learners

If you are still deciding on a direction, Uncodemy also runs Generative AI , Data Science and Playwright Automation Testing courses for Chennai and Tamil Nadu learners.

Portfolio/capstone project review:Your hands-on RAG applications, fine-tuning work and enterprise LLM capstone are reviewed and refined into a portfolio an interviewer will find credible.

Resume & LinkedIn:We shape your resume around LLM engineering keywords and measurable outcomes, and align your LinkedIn profile to match.

Interview preparation:System design for LLM applications, RAG pipeline debugging, fine-tuning trade-off questions and LLM engineering case studies specific to AI hiring.

Mock interviews:Sessions with industry trainers, followed by written feedback on what to improve.

Opportunity sharing:Relevant openings from our hiring network of 850+ companies are shared with you, with support through the application and interview process.

Continued support:This is active assistance, not a guarantee of employment — no training institute can honestly promise a job. Support continues after your program ends for learners who stay engaged with the process.

Review

4.7/5500+ Google Reviews

5/530+ Sulekha Reviews

4.7/5500+ Just Dial Reviews

5/5Facebook Reviews

4/5130+ Mouthshut

5/554+ Trustindex Reviews

Instructors

The people who actually teach this course

Uncodemy treats two roles as distinct that most institutes merge into one. Trainers handle the live sessions. Domain reviewers — separate experts who don't teach the batch — ensure the curriculum is technically accurate and current. For this program, curriculum and technical content are reviewed by Mr. Irshad, with extra review by Mr. Upendra Kumar Tiwari. This means the person teaching a course is not the only person checking whether its content is correct.

Every trainer and domain reviewer associated with Uncodemy is published by name, with their domain and background — you can look them up before you enrol rather than after.

Enroll in Our LLM Engineering Training for Chennai Learners Today!

Begin your LLM engineering journey with Uncodemy's course designed for Chennai learners. Through live online instructor-led sessions, you'll build practical command over LLM APIs, RAG pipelines, fine-tuning and LLM agents — reinforced with real projects and an enterprise LLM capstone. Structured training and career preparation, rolled into one program.

LLM Engineer Jobs and Career Scope in Chennai+

Chennai is one of India's largest IT and SaaS hubs — anchored by Old Mahabalipuram Road (OMR, also called the IT Corridor), Sholinganallur, Perungudi, Taramani and Ambattur, and home to a dense concentration of global capability centres, product companies, deep-tech startups and BFSI back-offices. Organisations across OMR, Guindy, Tidel Park, Ramanujan IT City, Ambattur, Porur, Velachery and Mount Road are layering in LLM-powered features — support chatbots, RAG-based knowledge assistants, document-processing systems and agentic AI workflows — creating demand for engineers who understand LLM APIs, retrieval pipelines, fine-tuning trade-offs and deployment. IT services, BFSI, fintech, healthcare, automotive-tech, SaaS, e-commerce and semiconductor-adjacent sectors in Chennai are also adopting GenAI, and they want engineers who can ship reliable LLM-powered applications rather than prototype demos. Interviews in Chennai typically weight system design for LLM applications, RAG pipeline design and debugging, and fine-tuning trade-offs most heavily. Candidates with software engineering or data/ML experience, combined with LLM engineering skills, are particularly well-positioned for these roles.

Job Roles After This Course+

LLM Engineer, AI Engineer, RAG/AI Application Engineer, Generative AI Engineer, ML Engineer (LLM-focused) and AI Solutions Architect (Junior) are the roles this course maps to. Each role involves building, fine-tuning or deploying LLM-powered applications, but they differ in the specific LLM components and tools used.

Role	Core Tools
LLM Engineer	LLM APIs, LangChain, Python, Vector DBs
AI Engineer	Python, Hugging Face, RAG, Fine-Tuning
RAG/AI Application Engineer	LangChain, Vector DBs, Embeddings
Generative AI Engineer	LLM APIs, Prompt Engineering, Python
ML Engineer (LLM-focused)	Hugging Face, Fine-Tuning, Evaluation Tools
AI Solutions Architect (Junior)	LangChain, Deployment Tools, Python

Most graduates start as an LLM Engineer, AI Engineer or RAG/AI Application Engineer and move into the others with two to four years of experience, as they build depth in a specific LLM component or domain. Progression typically involves owning larger systems, leading projects, and specialising in fine-tuning, agents or deployment architecture.

LLM Engineer Salary in Chennai — Role-wise+

Salaries in Chennai vary by experience level, sector and how strong your LLM engineering skills are. The gap between the lowest and highest earners at the same title is often wider than freshers expect.

Role	Salary Range
Entry-Level LLM Engineer	₹6–10 LPA
LLM Engineer / AI Engineer	₹10–18 LPA
Mid-Level LLM Engineer	₹15–25 LPA
Generative AI Engineer	₹10–20 LPA
RAG / AI Application Engineer	₹10–20 LPA
Senior LLM Engineer	₹20–35 LPA
Lead AI / LLM Engineer	₹25–45 LPA

Sources reviewed: AmbitionBox, Glassdoor, Indeed and Uncodemy placement records. Reviewed: September 2026.

Salary figures are presented as indicative market ranges, not guaranteed course outcomes. Actual offers vary by role, skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary or employment outcome.

Top Sectors Hiring LLM Engineers in Chennai+

Uncodemy works with 850+ companies in our hiring network across India, spanning large enterprises, mid-size firms and fast-growing startups, which gives learners exposure to more than one kind of hiring process and interview style. In Chennai specifically, LLM engineering demand is emerging in:

IT services and global capability centres — OMR, Tidel Park, Ramanujan IT City, Sholinganallur and Ambattur belt
Product companies and SaaS startups — building chatbots, AI assistants and RAG-based features
BFSI, fintech and insurance — deploying LLMs for risk, fraud detection and customer support
Healthcare, edtech and e-commerce — LLMs for personalisation, content and customer service
Automotive-tech, manufacturing-tech and semiconductors — LLM-driven knowledge systems, documentation and support applications

Each of these sectors tends to weight skills slightly differently, so as you get closer to placement, it helps to tailor your capstone project toward the one or two sectors you're most interested in.

How to Become an LLM Engineer — Step by Step+

Becoming an LLM engineer is a step-by-step skill build, not a single course, and skipping steps usually shows up later as gaps in interviews or on the job.

Python fundamentals— syntax, data structures, APIs and environment management.
ML/NLP basics— supervised learning, text preprocessing, embeddings.
Transformer architecture— attention, positional encoding, pre-training vs fine-tuning.
Prompt engineering— design patterns, structured outputs, evaluation.
LLM APIs & embeddings— API integration, streaming, vector databases.
RAG & application development— retrieval pipelines, LangChain, chat interfaces.
Fine-tuning, agents & deployment— LoRA, tool calling, evaluation and production deployment.

Typical timeline: 5–6 months of consistent study to reach interview-ready standard, assuming focused hours most days rather than occasional binge sessions.

How This Course Compares to Other AI Courses in Chennai+

Chennai has dozens of AI training options at very different price points, ranging from short weekend workshops to multi-month bootcamps. Here is an honest comparison, so you can judge the fit yourself rather than relying on marketing claims alone.

Factor	This Program
Duration	5–6 months
Learning hours	160+
Live sessions	100+
Tools	15+
Fine-tuning module	Dedicated 15-hour module
Capstone	20-hour Enterprise LLM project
Career support	Dedicated Career & Placement Assistance

Compared to Uncodemy's Generative AI & Agentic AI course (~3–4 months, focused on GenAI apps and autonomous agents) and AI Testing course (1.5 months, focused on testing LLMs, RAG and AI agents), this LLM Engineering program is the deepest, longest and most engineering-heavy option — it includes dedicated fine-tuning and deployment modules plus a 20-hour capstone. If your goal is a full LLM engineering career transition, this is the right fit. If you only need prompt-engineering upskilling, a shorter course may suit you better. If you are specifically building AI agents, the Generative AI & Agentic AI course is a closer match.

Where we are not the cheapest option: shorter self-paced courses cost less. If your goal is a quick LLM refresher rather than a full career transition, a shorter program may suit you better, and it's worth being honest with yourself about which of the two you actually need.

Online vs Offline vs Hybrid — Which Should You Choose?+

Offline classroom is available at our Delhi and Noida centres; Chennai learners typically join live online. Live online preserves the full instructor-led experience with no commute, and every session is recorded for revision. Offline fits learners near Delhi or Noida who prefer face-to-face interaction. Hybrid is not currently offered for this program. For Chennai-based working professionals, live online is usually the most practical route — real-time doubt clearing, hands-on labs and lifetime access to recordings, all without travelling.

Is this course right for you?+

If you want...	Recommend
Full LLM Engineering career transition	This program
Quick prompt-engineering upskilling only	Consider AI Testing or GenAI course
Already strong in traditional ML, want to specialise in LLMs	This program
Building AI agents specifically	Consider Generative AI & Agentic AI course

Frequently Asked Questions

1. What's the duration of the LLM Engineering course in Chennai?▼

The program is spread across 5–6 months and includes 160+ hours delivered over 100+ live sessions. You can pick a weekday or weekend batch.

2. Do I need prior Python or ML background?▼

Prior experience isn't mandatory. Python is taught from the ground up in Module 2, and foundational ML/NLP topics are covered in Module 3. You will need a laptop with reliable internet.

3. Which tools are covered in this course?▼

The curriculum includes 15+ tools: LLM APIs & SDKs (OpenAI/Gemini/Claude-style), LangChain, Vector Databases (FAISS/Chroma/Pinecone), Python, Hugging Face Transformers, Prompt Engineering Tools, Fine-Tuning Frameworks, Evaluation/Observability Tools, and Git & GitHub.

4. Does the syllabus cover fine-tuning and LLM agents?▼

Yes. Module 10 focuses on Fine-Tuning & Model Adaptation (15 hours), and Module 11 covers LLM Agents & Tool Calling (15 hours). Both include practical exercises.

5. What is the capstone project about?▼

The capstone is Module 13: Enterprise LLM Projects & Capstone (20 hours). You'll design and deploy a full LLM-powered application that integrates RAG, fine-tuning, agents and deployment.

6. Are classes live or pre-recorded?▼

Every one of the 100+ sessions is live and instructor-led. Session recordings are shared afterwards so you can revisit anything.

7. What is the fee for Chennai learners?▼

The program fee is ₹17,500 plus 18% GST — a total of ₹20,650. This fee applies uniformly across cities and modes.

8. Is EMI available?▼

Yes, EMI starts at ₹4,032 per month. Exact amount, tenure and eligibility depend on the payment/financing option chosen — your counsellor can confirm the details.

9. Can I book a demo class first?▼

Yes. A demo class lets you experience the trainer's teaching approach and review the curriculum before enrolling. Reach out to schedule one.

10. Do you provide placement assistance?▼

Yes — Dedicated Career & Placement Assistance includes resume building, LinkedIn optimisation, portfolio review, interview preparation, mock interviews and opportunity sharing via our 850+ company network. Employment is not guaranteed.

11. Can I land an entry-level LLM Engineer role after this course?▼

Entry-level LLM Engineer roles typically sit in the ₹6–10 LPA range (indicative). Actual outcomes depend on your skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary or employment outcome.

12. What is the salary of an LLM Engineer in Chennai?▼

Indicative market ranges: Entry-Level ₹6–10 LPA, Mid-Level ₹15–25 LPA, Senior ₹20–35 LPA, Lead ₹25–45 LPA. Actual offers vary by role, skills, experience, employer and market conditions.

13. Which roles can I target after completing this course?▼

You can target LLM Engineer, AI Engineer, RAG/AI Application Engineer, Generative AI Engineer, ML Engineer (LLM-focused), and AI Solutions Architect (Junior) roles.

14. Who should join this LLM Engineering course?▼

The course suits developers wanting to specialise in LLM-powered applications, data scientists/ML engineers upskilling into LLM engineering, aspiring AI engineers, and working professionals transitioning into GenAI/LLM roles.

15. Are the salary, placement and review figures on this page guaranteed?▼

No. These figures come from Uncodemy's records and public review platforms. Placement and salary outcomes vary by learner, skills, experience, location, employer and market conditions.

16. Is classroom training available in Chennai?▼

The course is delivered live online across India, including for Chennai learners. Classroom training for other courses is available at our Delhi and Noida centres. Chennai learners typically join live online.

17. Is the Uncodemy LLM Engineering certificate affiliated with OpenAI, Google or Anthropic?▼

No. This is an independent Uncodemy certificate — not affiliated with, endorsed by, or a credential of OpenAI, Google, Anthropic or any other LLM/vendor whose APIs are covered in this course.

Why Trust This Page? Discover Why Students Choose Uncodemy

How we maintain this page

Content written by:Mr. Rahul
Content rechecked and verified by: Mr. Irshad
Course curriculum verified by:Mr. Irshad
Technical content verified by: Mr. Irshad
Fee verified by: Admin Department
Placement figures sourced from internal placement records
Review counts verified against respective third-party platforms
Last reviewed: 25 Sep 2026

Written by:Mr. Rahul

Reviewed by: Mr. Upendra Kumar Tiwari

Final editorial check:Mr. Gangesh Gaunr, Director at Uncodemy

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LLM Engineering Training Course in Chennai by Uncodemy

Special Offer: LLM Engineering Program

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Lifetime Access

Dedicated Career & Placement Assistance

Certified Course

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Program Details

LLM Engineering Course in Chennai — Quick Facts

List of Placed Students Trained by Uncodemy

What is LLM Engineering?

LLM Engineering vs Generative AI vs Data Science

Course Overview

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Curriculum for LLM Engineering Training Course in Chennai

LLM Engineering Course Syllabus in Chennai

Module 1: LLM & Generative AI Foundations — 8 Hours

Module 2: Python for LLM Engineering — 10 Hours

Module 3: Machine Learning & NLP Fundamentals — 10 Hours

Module 4: Transformers & LLM Architecture — 12 Hours

Module 5: Prompt Engineering & Structured Outputs — 8 Hours

Module 6: Working with LLM APIs & SDKs — 10 Hours

Module 7: Embeddings & Vector Databases — 10 Hours

Module 8: Retrieval-Augmented Generation (RAG) — 15 Hours

Module 9: LLM Application Development — 12 Hours

Module 10: Fine-Tuning & Model Adaptation — 15 Hours

Module 11: LLM Agents & Tool Calling — 15 Hours

Module 12: LLM Evaluation, Optimization & Deployment — 15 Hours

Module 13: Enterprise LLM Projects & Capstone — 20 Hours

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Mr. Irshad

Get Your LLM Engineering Certification In Chennai

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LLM Engineer Jobs and Career Scope in Chennai+

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LLM Engineer Salary in Chennai — Role-wise+

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How to Become an LLM Engineer — Step by Step+

How This Course Compares to Other AI Courses in Chennai+

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Frequently Asked Questions

1. What's the duration of the LLM Engineering course in Chennai?▼

2. Do I need prior Python or ML background?▼

3. Which tools are covered in this course?▼

4. Does the syllabus cover fine-tuning and LLM agents?▼

5. What is the capstone project about?▼

6. Are classes live or pre-recorded?▼

7. What is the fee for Chennai learners?▼

8. Is EMI available?▼

9. Can I book a demo class first?▼

10. Do you provide placement assistance?▼

11. Can I land an entry-level LLM Engineer role after this course?▼

12. What is the salary of an LLM Engineer in Chennai?▼

13. Which roles can I target after completing this course?▼

14. Who should join this LLM Engineering course?▼

15. Are the salary, placement and review figures on this page guaranteed?▼

16. Is classroom training available in Chennai?▼

17. Is the Uncodemy LLM Engineering certificate affiliated with OpenAI, Google or Anthropic?▼

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