A 6-month live online Generative AI course that takes you from Python and machine learning 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 today’s open models like Llama 4, DeepSeek and Qwen3, then ship portfolio projects that prove to hiring teams you can design end-to-end GenAI systems.
12 modules • 200+ hours • 36+ tools • Certificate included • Placement support
Generative AI Training Overview
A quick snapshot of the format, duration, fee, core skills, projects, certificate and support you get when you join the program.
Format
Live Online Training
Duration
6 Months
Fee
₹64,999 (one-time)
Best For
Engineers, developers, ML learners and working professionals
Core Skills
LLMs, RAG, fine-tuning, agents, multimodal AI and model serving
Projects
RAG app, fine-tuned LLM, multimodal app and production API
Certificate
Included
Support
Placement and career support
Quick Answer
It’s a 6-month live online program that takes you from machine learning and deep learning foundations all the way to production Generative AI. You learn transformers, LLMs, RAG, fine-tuning with LoRA and QLoRA, multimodal AI and AI agents — and you build real projects at every stage. You finish with a course completion certificate, placement support and a portfolio you can actually show in interviews. The fee is ₹64,999.
Core Concept
Generative AI is a category of artificial intelligence that creates new text, images, code, audio, and video by learning patterns from large datasets. Unlike traditional ML that classifies or predicts, generative models produce original outputs. The core architectures powering this field today are:
Large Language Models
GPT, Claude, Gemini, Llama 4, DeepSeek, Qwen3
Diffusion Models
FLUX.1, Stable Diffusion 3.5, Sora-class video
Vision-Language Models
Qwen3-VL, Llama Vision, InternVL, CLIP
Code Generation
Qwen3-Coder, DeepSeek-Coder, StarCoder2
Your Portfolio
You graduate with a portfolio that grows from AI foundations to production GenAI systems. You start with model training and evaluation, then move into transformers, RAG, fine-tuning, multimodal AI and a production-style agentic AI system.
Build a practical ML workflow with data preparation, model training, evaluation metrics and experiment comparison so you understand how AI systems are measured before moving into GenAI.
Work with neural networks, CNN/RNN concepts and transformer-based architectures to understand how modern AI models learn from text, images and sequences.
Build a retrieval-augmented generation system with document ingestion, chunking, embeddings, vector search, reranking, source citations and grounded LLM responses.
Adapt an open-source language model using LoRA / QLoRA concepts, evaluate outputs on custom tasks and understand when fine-tuning is better than prompting.
Build an AI system that works with text, image or document inputs using vision-language models and multimodal inference workflows.
Build a production-style GenAI system with agents, tool use, RAG or memory support, API serving, logging, evaluation checkpoints and a demo-ready workflow.
It’s built for developers, ML learners, freshers and working professionals who want a clear, structured path into LLMs, RAG, fine-tuning, multimodal AI, agents and production serving.
Build production-ready GenAI applications on top of your coding background.
Extend ML and deep learning fundamentals into modern Generative AI systems.
Follow a structured path from foundations into deployable GenAI projects.
Upskill without losing the technical depth needed for real AI work.
Basic Python is helpful, but you do not need prior LLM or deep learning experience to start. The course begins with ML and deep learning fundamentals before moving into advanced Generative AI systems.
Helpful to have
You will build up during the course
By the end you’ve built the practical skill set hiring teams expect from an AI or Generative AI Engineer — not just theory, but the ability to build, fine-tune, ground and ship real AI systems.
“AI Engineering” is defined more by job skills than by a single syllabus — building, fine-tuning, retrieving, serving and evaluating AI systems. This Generative AI course covers that full skill set end to end, so it doubles as a practical AI Engineering path. If your goal is mainly to build and ship AI applications, the AI Developer Course is the application-focused companion.
Build the mathematical and machine learning base needed for applied Generative AI work.
Understand the deep learning building blocks that lead into modern transformer systems.
Learn attention, tokenization, prompting, and model behavior across modern language models.
Work with ViTs, VLMs, and multimodal pipelines that combine text and visual understanding.
Build retrieval pipelines with chunking, embedding, filtering, ranking, and grounded responses.
Create tool-using agent systems that can reason, call APIs, and orchestrate multi-step tasks.
Customize models with LoRA, QLoRA, and related techniques for domain-specific use cases.
Reduce inference cost and improve deployment efficiency with practical optimization methods.
Serve production-ready models through APIs with scalable inference patterns and deployment workflows.
Measure reliability, quality, and safety using structured evaluation and monitoring workflows.
Ship applications that combine text, image, and speech inputs in one AI product workflow.
Connect models, serving, infrastructure, and product-facing APIs into deployable systems.
The Generative AI tools and frameworks covered in this course are the same ones used in production AI systems at leading companies. You will work hands-on with 36+ tools across training frameworks, orchestration libraries, vector databases, serving infrastructure, API providers, and observability platforms — the toolkit behind everything you train and ship next.
Core deep-learning framework for model training and research
Structured training loops, multi-GPU scaling, and distributed training
Pre-trained models, tokenizers, and training pipelines for NLP and vision
Diffusion model pipelines for image, video, and audio generation
Parameter-efficient fine-tuning (LoRA, QLoRA) and RLHF/DPO training
Distributed training and inference optimization for billion-parameter models
Chain LLMs with tools, APIs, memory, and external data sources
Index, retrieve, and query large datasets for RAG pipelines
Build stateful, multi-step agent workflows as directed graphs
Multi-agent orchestration with role-based collaboration and planning
End-to-end NLP framework for building search and QA pipelines
Microsoft SDK for integrating LLMs with enterprise plugins and planners
Managed vector DB for production-scale semantic search with metadata filtering
Fast approximate nearest-neighbor search for dense vectors (Meta AI)
Open-source vector DB with advanced filtering and payload support
Lightweight, developer-friendly vector store for rapid prototyping
Multi-modal vector search with GraphQL interface and hybrid search
Cloud-native vector database built for billion-scale similarity search
High-throughput LLM serving with PagedAttention and continuous batching
Hugging Face production inference server with quantization support
High-performance async API framework for model endpoints
Containerize models for consistent cross-environment deployment
Orchestrate and auto-scale containerized AI workloads in production
NVIDIA multi-framework serving with dynamic batching and GPU scheduling
Access GPT-4o, embeddings, function calling, and structured outputs
Multimodal reasoning, long-context, and grounded generation
Constitutional AI, tool use, and extended-context conversations
Build interactive web demos for AI models in minutes
Data apps and dashboards for AI model outputs and prototyping
Build production-ready conversational AI interfaces with streaming
Trace, debug, and evaluate LLM chain performance end to end
Open-source LLM observability, cost analytics, and prompt management
Evaluate RAG pipeline quality — faithfulness, relevance, context recall
Unit-test LLM outputs with customizable metrics and CI integration
Experiment tracking, model versioning, and hyperparameter sweeps
Real-time LLM observability, drift detection, and embedding analysis
With that stack in hand, you train and fine-tune real open models yourself, instead of only calling them through an API. These are the four model families you work with hands-on.
Frontier APIs like Claude, GPT and Gemini set the bar — you learn to match much of it with open models you can actually train, from 7B to 600B+ parameters.
Models that reason across images, text and speech — powering visual QA, document understanding and cross-modal search.
Text-to-image, text-to-video and creative AI — the generative media stack driving modern design and content pipelines.
Purpose-built for software engineering — code generation, debugging, refactoring and inline copilot experiences.
Once you’ve picked your models, the real work is making them accurate, controllable, fast and production-ready. You move through four technique areas — adapting models, aligning them, grounding them with retrieval, and optimising them for serving.
LoRA · QLoRA · PEFT · DAPT · SFT
What: Inject low-rank adapters into target layers instead of retraining the entire model.
Why: Fine-tune billion-parameter models on consumer GPUs with rapid iteration and minimal compute.
DPO · GRPO · RLVR · KTO · RLHF (PPO) · RLAIF
What: Steer behavior and reasoning toward preferred, verifiable outputs using preference pairs and reward signals — the same methods behind DeepSeek-R1, Qwen and Kimi.
Why: Safer, more controllable and better-reasoning models — critical for production copilots and enterprise deployment.
Hybrid RAG · Graph-RAG · Fusion RAG · Re-ranking
What: Ground LLM responses with external knowledge via chunking, embedding, retrieval, and citation.
Why: Accurate, hallucination-resistant answers for legal, healthcare, and enterprise QA systems.
KV-Cache · Speculative Decoding · MoE Routing · Quantization
What: Maximize throughput and minimize latency with attention-aware caching, draft-model decoding, and expert routing.
Why: Production-grade speed at lower cost — serve thousands of concurrent requests efficiently.
This is the high-level map of the program — four phases across 24 weeks. You start from Python with no prior Generative AI experience, then move through transformers, LLMs, RAG, fine-tuning, multimodal AI and agents. For every topic in detail, see the full syllabus below; for the week-by-week version, open the complete Generative AI roadmap.
Weeks 1–6 · Start from Python — no prior GenAI needed.
Week 1–2
Python for AI, core math, and ML basics to build a solid base.
Week 3–4
Perceptrons, back-propagation, CNNs and RNNs, built in PyTorch.
Week 5–6
Vision and NLP models you train, then deploy with ONNX.
Weeks 7–12 · Transformers, LLMs and image generation.
Week 7–8
Autoencoders, VAEs, GANs and diffusion model foundations.
Week 9–10
Transformers, attention, tokenization and core LLM architectures.
Week 11–12
Vision-language models and text-to-image generation in practice.
Weeks 13–18 · Multimodal AI, fine-tuning and RAG.
Week 13–14
Fusion patterns and multimodal agents across text and images.
Week 15–16
SFT, LoRA, QLoRA and alignment with DPO, GRPO and RLHF.
Week 17–18
Embeddings, vector databases and production-grade RAG pipelines.
Weeks 19–24 · Serving, reasoning RL and agentic AI.
Week 19–20
Quantization, vLLM/TGI inference and scalable serving stacks.
Week 21–22
Reasoning RL (RLVR), chain-of-thought, function calling and evaluation.
Week 23–24
Agent types, frameworks and an end-to-end multi-agent capstone.
By the end, you’ll be ready for roles like GenAI Engineer, LLMOps Specialist, or AI Research Developer — capable of designing systems like RAG-powered assistants, multimodal AI apps, or enterprise copilots from scratch.
The Generative AI course syllabus is a comprehensive, section-by-section breakdown of every topic, tool, and technique covered across 24 weeks of live instructor-led training. It includes ML foundations, neural networks, deep learning, transformers, LLMs, RAG, fine-tuning, multimodal AI, agents, quantization, model serving, and deployment.
Generative AI Course with Certification
Upon completing the Generative AI course requirements, you receive a School of Core AI course completion certificate you can add to LinkedIn and your portfolio. It reflects hands-on work across LLMs, VLMs, diffusion models, RAG pipelines, fine-tuning, alignment, and production-grade serving.
Sample certificate
CERTIFICATE
OF ACHIEVEMENT
THIS IS TO CERTIFY THAT
YOUR NAME
Date : 25th Jan 26
Has Successfully Completed The
6-Month Comprehensive Generative AI Training Program
Conducted By The School Of Core AI.
This Intensive Program Included Hands-On Training In Python, Deep Learning, Transformers, LLMs, VLMs, Stable Diffusion, RAG Pipelines, Fine-Tuning (LoRA/QLoRA), RLHF/DPO/GRPO Alignment, Model Serving (vLLM/TGI), And Agentic AI Fundamentals.
Aishwarya Pandey
Founder and CEO
Share your certificate on LinkedIn, include it in your portfolio, or present it during interviews.
This Generative AI course is built for engineers who want production-level depth. Here is what the program gives you — and, just as important, why each part matters when you are landing a role and doing real GenAI work.
| Focus Area | What’s Included | Why It Matters |
|---|---|---|
| LLMs & RAG Modules | ✔ End-to-end RAG: retrieval setup, tool use, function calling, grounding & citations. | Grounded retrieval with citations is the GenAI skill production teams hire for first. |
| MCP (Model Context Protocol) & Tool Interop | ✔ Portable tool adapters, unified context, and vendor-neutral integration patterns. | Vendor-neutral integration keeps your apps portable instead of locked to one provider. |
| Hybrid RAG & Re-Ranking | ✔ BM25 + dense + metadata filters with re-ranking, multi-hop queries, and eval. | Hybrid retrieval is what turns a fragile demo into reliable, citable answers. |
| Project-Based Curriculum | ✔ 20+ projects: fine-tuning, multimodal apps, domain RAG, internal copilots. | A real project portfolio is what hiring teams review most closely. |
| Deployment & Scaling | ✔ FastAPI, Docker, Kubernetes; vector DBs; CI/CD and autoscale practices. | Shipping and scaling is the line between a notebook demo and a deployable system. |
| Tooling Ecosystem Mastery | ✔ Hugging Face, Diffusers, OpenAI SDKs, Pinecone, Chroma, FAISS. | You practise on the same stack production GenAI teams use every day. |
| Evaluation, Tracing & Guardrails | ✔ RAGAS/DeepEval, LangSmith/LangFuse, policy tests, PII filters, regression suites. | Evaluation and safety are now mandatory for enterprise GenAI work. |
| Live Mentorship & Expert Sessions | ✔ Weekly live classes, 1:1 mentorship, office hours with AI engineers. | Live feedback from working engineers is how you unblock fast and learn current practice. |
| Placement Support & Career Guidance | ✔ Portfolio reviews, resume feedback, interview prep, and a course completion certificate. | Career support turns new skills into real interviews and role transitions. |
Clear, one-time pricing for the full 6-month program — guided projects, certificate and learner support are all included.
One-time payment
₹64,999
6 months • Live ILT • Capstone • Certificate
Generative AI course fees are 64,999 INR for a 6 month live instructor-led training program with sessions, guided projects, capstone demo, and a course completion certificate.
The course is structured to help serious learners build a credible portfolio, prepare for interviews, and continue learning alongside their current work or academic schedule.
Career Support
Placement support is built around portfolio quality, interview readiness, and applied project work. The focus is on helping you present your Generative AI skills clearly rather than making unsupported hiring promises.
Learner Fit
This course is suitable for working professionals who want depth without losing flexibility. Live online instruction, mentor support, and recordings make it easier to keep momentum while balancing a job.
Career Outcomes
This course is designed to prepare you for roles like GenAI Engineer, AI Engineer, LLM Engineer, AI Application Engineer and Multimodal AI Engineer. Each one maps to a cluster of skills you practise during the program, so you can see exactly where your learning leads.
Skills that map here
LLMs, RAG, Agents
Skills that map here
ML, Deep Learning, Transformers
Skills that map here
Fine-tuning, Quantization
Skills that map here
APIs, Serving, Deployment
Skills that map here
Multimodal AI, VLMs
Career outcomes depend on your current background, portfolio quality, interview preparation and hiring market conditions.
These are real learners who finished the full program — ML and deep learning foundations, LLMs, fine-tuning, RAG and deployment — and went on to work as Gen AI, deep learning and AI research engineers. Here is what stayed with them.
Hear real experiences from professionals who've completed this course
If you want to go deeper after finishing this Generative AI course, these are the four most relevant next-step courses for application development, model-side depth, agent workflows, and production AI operations.
For software developers who want to build AI applications using LLM APIs, RAG, agents and backend workflows.
View AI Developer CourseFor learners who want deeper LLM understanding, prompting, LLM workflows and model-side language AI concepts.
View Large Language Model CourseFor learners who want to build agent workflows, multi-agent systems and real-world AI automation.
View Agentic AI CourseFor learners who want production AI operations, monitoring, reliability, observability and automation.
View AIOps CourseBoth are strong, in-demand paths and they overlap a lot. There is no wrong choice — this quick comparison just helps you pick the one that matches your goal first.
| How they compare | Generative AI Course | AI Developer Course |
|---|---|---|
| Best for | Engineers who want full-stack GenAI depth across models, RAG, fine-tuning and serving. | Developers who want to build and ship AI applications fast using existing models and APIs. |
| Core focus | How modern AI models work, and how to adapt, evaluate and deploy them. | How to assemble LLMs, RAG and agents into production application features. |
| You build | Fine-tuned LLMs, multimodal apps, domain RAG systems and served endpoints. | AI-powered product features and backends built around LLM APIs. |
| Depth vs speed | Deeper model and infrastructure understanding across the GenAI stack. | Faster path to usable AI apps, with less focus on model internals. |
| Typical next role | GenAI Engineer, AI Engineer, LLM Engineer. | AI Application Developer, AI Software Engineer. |
More GenAI comparisons
The questions people ask most before enrolling — course fit, prerequisites, syllabus depth, career outcomes, fees, certification and placement support.
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