SCHOOLOFCOREAI

LIVE ONLINE • INSTRUCTOR-LED • 6-MONTH GENERATIVE AI PROGRAMME

Generative AI Course in Mumbai

A 6-month live online Generative AI course that takes you from Python and ML foundations to building and deploying real LLM, RAG, multimodal and agentic AI systems. You don't just call an API — you train and fine-tune open models from the Llama, DeepSeek and Qwen families, then ship portfolio projects that prove you can design end-to-end GenAI systems.

Designed for software developers, ML engineers, data scientists, and working professionals in Mumbai's engineering ecosystem.

Agentic AIMCPMultimodal AIRAG PipelinesFine-Tuning & AlignmentDiffusion ModelsProduction Deployment
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12 modules · 200+ hours · 36+ tools · Certificate included · Placement support

Course Snapshot

Programme duration6 Months
One-time fee₹64,999
Instructor-led formatLive Online
Models, frameworks & platforms36+ Tools
Mentorship & career supportIncluded
Admissions Open · Next cohort dates confirmed on enquiry

Page Definition

What Is the Generative AI Course in Mumbai?

The Generative AI Course is a 6-month live online programme that covers the full GenAI engineering stack — from ML and deep learning foundations through transformers, LLMs, RAG, fine-tuning with LoRA and QLoRA, multimodal AI, evaluation, and production deployment. You build real GenAI systems, not just prompt demos.

The programme is live online — you join from Mumbai or anywhere. It covers ML and deep learning foundations, transformers, LLMs, RAG, fine-tuning with LoRA and QLoRA, multimodal AI, evaluation, and production deployment over 6 months.

Who Should Join

Who Should Take This Generative AI Course in Mumbai?

This programme is for engineers and technical professionals who want in-depth GenAI engineering — not surface-level API tutorials. If you work with software, ML, data, or cloud and want to go deep into model foundations, RAG, fine-tuning, multimodal AI and production deployment, this course builds the missing layer.

Recommended foundation

Basic Python is helpful. No prior LLM or deep learning experience is needed — the course builds ML and deep learning foundations before advancing to GenAI topics.

Not a fit if

This is not a prompt engineering workshop, a no-code AI course, or a surface-level API tutorial. It is a 6-month engineering programme that goes from foundations to production.

GenAI Capability Studio

What You Actually Learn in Generative AI

Generative AI is not just prompting. It is a stack of engineering capabilities — from understanding foundation models to grounding, adapting, expanding, evaluating, and shipping them. Each capability has a responsibility and an engineering question.

Understanding Transformer Architectures

TOKENS→EMBEDDINGS→ATTENTION→TRANSFORMER→LLM

Responsibility

Understand how transformer architectures, tokenization, embeddings, and attention work — the foundation for everything else.

Engineering Question

How do you reason about model behaviour without understanding the architecture?

Tools You Work With

PyTorchHugging Face TransformersTokenizers

UNDERSTAND — Understanding Transformer Architectures

GROUND — Retrieval-Augmented Generation and Grounding

ADAPT — LLM Fine-Tuning with LoRA and QLoRA

EXPAND — Multimodal and Vision-Language Models

EVALUATE — Generative AI Evaluation

SHIP — LLM Serving and Production Deployment

ORCHESTRATE — Agentic AI Fundamentals

Curriculum

Generative AI Course Curriculum and Learning Path

The curriculum follows a layered learning model — each layer builds on the previous one, moving from model foundations to production deployment.

LAYER 01

Model Foundations

ML Foundations and Deep Learning

Python essentials, mathematical foundations, ML basics, neural networks, CNNs, RNNs, transfer learning

Module 1: Foundation Refresher, Module 2: Neural Network Essentials, Module 3: Applied Deep Learning

LAYER 02

Generation

Generation and LLM Fundamentals

Transformer architecture, tokenization, attention, LLM fundamentals, GANs, vision transformers, NLP with transformers

Module 4: Generative AI Fundamentals, Module 5: LLMs Demystified, Module 6: GenAI for Vision

LAYER 03

Grounding

Retrieval-Augmented Generation

RAG architecture, chunking, embedding, retrieval, reranking, vector databases (Pinecone, FAISS, Qdrant), citations

Module 9: Retrieval-Augmented Generation

LAYER 04

Adaptation

Fine-Tuning GenAI Models

LoRA, QLoRA, PEFT, SFT, DPO, RLHF, quantization, pruning, diffusion models, fine-tuning stable diffusion

Module 8: Fine-Tuning GenAI Models

LAYER 05

Multimodal

Multimodal AI Architectures

Vision-language models, CLIP, multimodal generation, image captioning, document intelligence, VLMs

Module 7: Multimodal AI Architectures

LAYER 06

Production

Quantization, Serving and Deployment

Quantization, vLLM, TGI, FastAPI serving, Docker, Kubernetes, evaluation, RAGAS, DeepEval, agents introduction

Module 10: Quantization & Serving, Module 11: Reasoning & RLHF, Module 12: Agentic AI Introduction

Explore the Complete Generative AI Curriculum →

Projects

Generative AI Projects You Will Build

You build 6 portfolio-grade GenAI systems across the programme — each one a real engineering project, not a tutorial demo. These are the projects you bring to interviews.

AI Foundations and Model Evaluation Lab

Problem: Before building GenAI systems, you need to understand how models are evaluated and compared — accuracy, loss, generalization, and overfitting.

Architecture: ML pipeline → model training → evaluation metrics → comparison framework → visualization.

Engineering decisions: Choosing evaluation metrics, setting up reproducible experiments, comparing model variants.

Evaluation: Cross-validation, metric selection, overfitting detection, baseline comparison.

What gets reviewed: Pipeline correctness, metric selection rationale, experiment reproducibility.

PyTorchPythonWeights & Biases
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Tools and Models

Generative AI Tools, Models and Frameworks Covered

Every tool in this programme answers a capability question: why is this tool used, and what part of the GenAI stack does it serve?

Model Engineering

Frameworks for building, training, and adapting generative models.

PyTorchPyTorch LightningHugging Face TransformersHugging Face DiffusersPEFT/TRLDeepSpeed/FSDP

Grounding and RAG

Orchestration frameworks and vector databases for retrieval-augmented generation.

LangChainLlamaIndexPineconeFAISSQdrantChromaDB

Multimodal

Models and frameworks for vision-language and multimodal AI.

CLIPVLMsOpen Vision ModelsInternVLHugging Face Diffusers

Evaluation

Tools for measuring retrieval quality, generation quality, and safety.

RAGASDeepEvalLangSmithLangFuseWeights & BiasesArize Phoenix

Serving and Deployment

Infrastructure for serving and deploying GenAI systems in production.

vLLMTGIFastAPIDockerKubernetesTriton Inference Server

APIs and UI

Model APIs and interface tools for building GenAI applications.

OpenAI APIGoogle Gemini APIAnthropic Claude APIGradioStreamlitChainlit
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Course Comparison

Generative AI vs AI Developer and Agentic AI Courses

SCAI offers multiple AI engineering programmes. Understanding the scope of each helps you choose the right path.

AI Developer Course

Build LLM-powered software — APIs, RAG applications, agent backends. Application-focused, faster pace.

Learn more

Generative AI Specialization (this course)

Models + RAG + fine-tuning + multimodal + evaluation + production. Broader and deeper GenAI engineering.

Agentic AI

Stateful agents, tool use, multi-agent orchestration, and production agent systems.

Learn more

LLMOps

Production lifecycle for LLM systems — serving, monitoring, drift, and operations.

Learn more

AIOps

Broader production AI stack combining ML, LLM, and agent operations.

Learn more

Mumbai Engineering Context

Learning Generative AI From Mumbai

Mumbai has a strong fintech, media, and enterprise IT ecosystem. Companies in BFSI, media, and product startups are building production GenAI systems that require engineers who understand model foundations, RAG, fine-tuning, and deployment.

Live Online Generative AI Training for Mumbai Learners

The programme is fully live online. Mumbai-based engineers join real-time sessions without commuting. The content is contextualized for Mumbai's fintech and media ecosystem — but you can join from anywhere.

Where Generative AI Skills Fit in Mumbai Engineering Work

  • · Fintech and BFSI — Razorpay, CRED, HDFC building AI-powered financial products
  • · Media and content — Disney+ Hotstar, Zee5 building personalization and content AI
  • · Enterprise IT — TCS, L&T Infotech, Accenture, Capgemini building enterprise GenAI
  • · Product startups — GenAI features in consumer and B2B applications

Local Engineering Ecosystem

AI and Engineering Companies in Mumbai

TCS

Enterprise AI

Accenture

AI Engineering

L&T Infotech

AI Infrastructure

Razorpay

Fintech AI

CRED

Fintech AI Features

Disney+ Hotstar

Media AI & Personalization

HDFC Bank

BFSI AI

Capgemini

Cloud AI

Mphasis

Enterprise AI

Zensar

AI Product Engineering

Tiger Analytics

AI & Analytics

Fractal Analytics

AI Consulting

Companies shown for local technology-ecosystem context. No hiring, placement or training partnership is implied unless explicitly stated.

How the Program Is Taught

How the Generative AI Program Is Taught

The programme follows an Explore → Implement → Generate → Compare → Evaluate → Refine loop. You explore concepts in live sessions, implement systems in projects, generate outputs, compare approaches, evaluate quality, and refine based on feedback.

EXPLORE→IMPLEMENT→GENERATE→COMPARE→EVALUATE→REFINE

Live Technical Teaching

Live instructor-led sessions on each GenAI capability — not recorded videos.

Guided Model and Application Builds

Each project builds a connected piece of the GenAI stack, not isolated demos.

Evaluation and Failure Analysis

You learn to evaluate GenAI systems — retrieval quality, generation quality, safety — and debug failures.

Project Reviews and Technical Feedback

Mentors review your work against production standards. You learn to explain and defend your engineering decisions.

Certification & Career

Generative AI Certification and Career Support

Generative AI Course Certificate

Course completion certificate from School of Core AI, awarded after completing all assignments and the capstone project.

Portfolio Evidence

Your certificate is backed by 6 reviewed, production-ready GenAI projects — RAG systems, fine-tuned models, multimodal apps, and production deployments.

Career Support

Resume review, GitHub and project presentation, mock interviews, interview preparation, job-search guidance, and referral connections where available.

No Placement Guarantee

We do not guarantee placement. We prepare you with real GenAI engineering skills, portfolio evidence, and interview readiness.

Course Fee and Cohort

Generative AI Course Fee, Format and Next Cohort in Mumbai

The fee is a one-time payment for the full 6-month live programme. Mentorship, career support, and certificate are all included — no hidden costs.

FEE

₹64,999

DURATION

6 Months

FORMAT

Live Online

LEVEL

Intermediate to Advanced

COHORT

Admissions Open

MENTORSHIP

Included

What the Program Fee Covers

  • · 6-month live instructor-led programme
  • · 6 portfolio-grade GenAI projects
  • · 36+ tools, models, and frameworks
  • · Mentor support and engineering reviews
  • · Career preparation: resume, GitHub, mock interviews
  • · Generative AI Course Certificate

Best For

  • · Software developers moving into GenAI engineering
  • · ML engineers extending into LLMs and fine-tuning
  • · Data scientists moving into generative models
  • · Working professionals wanting structured, live progression

Career support is included — resume review, GitHub and project presentation, mock interviews, interview preparation, job-search guidance, and referral connections where available. No placement guarantee.

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FAQ

Generative AI Course in Mumbai — FAQs

Is the Generative AI course in Mumbai online or classroom-based?
It is a live online, instructor-led programme. You join from Mumbai (or anywhere) and participate in real-time sessions, mentor reviews, and project builds. There is no in-person requirement.
What roles can I target after the Generative AI course in Mumbai?
GenAI Engineer, AI Engineer, LLM Engineer, AI Application Engineer, and Multimodal AI Engineer. Mumbai's fintech, media, and enterprise companies — including TCS, Accenture, Razorpay, and Disney+ Hotstar — actively hire for these roles.
Is there an EMI or installment option for the fee?
Yes, EMI options are available. Contact our team for current installment plans and payment details.
How is this Generative AI course different from other institutes in Mumbai?
The difference is engineering depth. You build the full GenAI stack — model foundations, RAG, fine-tuning with LoRA/QLoRA, multimodal AI, evaluation, and production deployment — as connected projects, not isolated tool tutorials. Sessions are live, projects are reviewed against production criteria, and the goal is work you can explain and defend in interviews.
Do I need to be in Mumbai to attend this course?
No. The programme is fully live online. While the content is contextualized for Mumbai's fintech and media ecosystem, you can join from anywhere in India or internationally.
What is the Generative AI engineer salary in Mumbai?
GenAI engineers in Mumbai typically earn ₹10L to ₹20L per year depending on experience and background. Senior roles with production GenAI experience can reach ₹25L+. Mumbai's fintech and enterprise companies offer competitive compensation. Salaries vary by company, model engineering skills, and production deployment experience.
What is the Generative AI Course?
A 6-month live online programme covering the full GenAI engineering stack — ML and deep learning foundations, transformers, LLMs, RAG, fine-tuning with LoRA and QLoRA, multimodal AI, evaluation, and production deployment. You build 6 portfolio-grade GenAI projects across the programme.
Do I need machine learning or deep learning experience?
Basic Python is helpful. No prior ML, deep learning, or LLM experience is needed — the course builds ML and DL foundations in the first phase before advancing to Generative AI topics.
Does the course cover RAG?
Yes. RAG is a core capability — you learn chunking, embedding, retrieval, reranking, context construction, and evaluation. You build a full RAG knowledge system as one of your portfolio projects, using LangChain, Pinecone/FAISS, and RAGAS for evaluation.
Will I learn LLM fine-tuning?
Yes. You learn parameter-efficient fine-tuning with LoRA and QLoRA, using PEFT/TRL and Hugging Face Transformers. You build a fine-tuned LLM assistant as a portfolio project, evaluating it against the base model.
Does the program cover LoRA and QLoRA?
Yes. LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) are covered as part of the fine-tuning module. You learn when to use each, how to configure them, and how to evaluate whether fine-tuning improved the model for your specific use case.
Will I learn multimodal AI?
Yes. Multimodal AI is a dedicated layer covering vision-language models, CLIP, and multimodal generation. You build a multimodal AI system as a portfolio project, working with text and image inputs together.
Does the course include AI agents?
Yes, as an introduction. Agentic AI fundamentals are covered in the final phase — tool use, planning, and multi-step reasoning with LangGraph and CrewAI. This is an application capability, not the main focus of the programme.
Will I learn Generative AI evaluation?
Yes. Evaluation is a core capability — you learn to measure retrieval quality, generation quality, and safety using RAGAS, DeepEval, LangSmith, and LangFuse. Every project includes evaluation as part of the review criteria.
Does the program cover LLM serving and deployment?
Yes. You learn to serve GenAI systems using vLLM, TGI, FastAPI, Docker, and Kubernetes. The final portfolio project includes a production GenAI system with serving infrastructure and monitoring.
Generative AI vs AI Developer Course — which should I choose?
AI Developer Course is application-focused — building LLM-powered software with APIs, RAG, and agents at a faster pace. Generative AI Specialization is broader and deeper — ML foundations, transformers, fine-tuning, multimodal AI, evaluation, and model serving. If you want to understand and adapt the models themselves, choose GenAI. If you want to build applications on top of them, choose AI Developer.
Generative AI vs Agentic AI — what is the difference?
Generative AI covers the full stack — models, RAG, fine-tuning, multimodal, evaluation, and serving. Agentic AI focuses specifically on autonomous agent systems — tool use, planning, multi-agent orchestration, and production agent architectures. GenAI introduces agents as one capability; Agentic AI goes deep on agents.
What projects will I build?
6 portfolio projects: AI Foundations Lab, Deep Learning and Transformer Workflow, RAG Knowledge System, Fine-Tuned LLM Assistant, Multimodal AI System, and a Production GenAI System with agents. Each project is reviewed against production engineering criteria.
How long is the program?
6 months, live online, instructor-led. The programme covers 4 phases, 12 modules, and 24 weeks of structured learning with weekday and weekend batch options.
What is the course fee?
₹64,999 plus applicable taxes. This is a one-time payment for the full 6-month programme, including mentorship, career support, and certificate. EMI options are available — contact our team for details.

Generative AI Course Reviews

Honest feedback from working professionals who upskilled with SCAI.

4.9/ 5

Read Generative AI Course reviews on Google — real experiences from learners covering mentorship quality, project depth, and career transitions.

Next Step

Get the Generative AI Course Syllabus and Fee Details

Tell us your current role, technical background, and what you want to build with Generative AI. The team can help you decide whether this 6 months programme is the right next step for your career in Mumbai.

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