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Generative AI Course — Build What Today’s Systems Use

Learn how modern GenAI works end to end. We cover LLaMA 3.1 / 4, DeepSeek MoE, Claude 3, Gemini 1.5 and the stack behind them: training, alignment, retrieval, serving, and optimization.

You’ll build hybrid RAG with re-ranking, fine-tune with QLoRA, and align with DPO / PPO / IPO. You’ll also learn KV-cache tricks, speculative decoding, and deployment with vLLM / TGI.

This is not no-code. It’s real GenAI engineering.

LLaMA 4DeepSeek MoEClaude 3Gemini 1.5SLMs100K+ ContextHybrid RAG + Re-rankingSpeculative DecodingKV Cache TricksLoRA / QLoRADPO / PPO / IPOvLLM / TGI Deployment

Build real architectures • Train, align, retrieve, serve • Interview-ready skills

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Perks Of Learning Generative AI Course

Plug Into the GenAI Vanguard

Access an elite network of engineers, researchers, and innovators shaping the future of Generative AI across LLMs, VLMs, and Agents.

Industry-Trusted Certification

Earn a GenAI Specialization Certificate that signals your readiness to build with cutting-edge models like LLaMA, Qwen, and Mamba.

Flexible, Deep Learning Journey

Master GenAI at your own pace with a modular curriculum packed with projects, mentorship, and toolchain walkthroughs.

Master Tools That Build the Future

Get hands-on with LangChain, LlamaIndex, Vector DBs, FastAPI, and LangGraph to architect AI-powered systems and agents.

Real-Time Research Access

Stay months ahead with curated updates on LLM architectures, fine-tuning breakthroughs, and the latest in multimodal AI.

AI Career Launchpad

Unlock portfolio reviews, 1:1 resume rewrites, mock interviews, and job connections tailored to AI engineering roles.

Mentorship from Core AI Engineers

Learn directly from engineers building real-world GenAI systems at top startups, labs, and AI-driven product teams.

Deploy Real GenAI Products

Build and ship production-grade GenAI apps—chatbots, image models, RAG workflows—with a GitHub-ready portfolio.

Train with Proven AI Stacks

Build confidence with real-world frameworks: PyTorch, Hugging Face, Redis, CrewAI, AutoGen, and vectorized retrieval.

Not sure where to start? Pick the path that fits your background — Mastering LLMs, AI Developer, or Data Science + GenAI Foundations.

Specialist

Mastering LLMs (LLM-Only Track)

Deep specialization in large language models — training, fine-tuning, alignment, and high-throughput serving.

  • •LLaMA 4 / DeepSeek / Gemini; long-context
  • •Fine-tuning: LoRA/QLoRA, RLHF, DPO/ORPO
  • •Serving: vLLM/TGI, KV cache, speculative decoding
  • •Eval & safety: RAGAS, DeepEval, guardrails
Developer

AI Developer (Apps & Integrations)

For devs who want to ship apps — agents, RAG, multimodal UX, and real deployments.

  • •LangChain/LlamaIndex, MCP, tool/function calling
  • •Hybrid RAG + re-ranking; Pinecone/FAISS/Chroma
  • •VLM + Diffusion integrations; streaming UX
  • •Deploy: FastAPI, Docker, vLLM/TGI, observability
Beginner / Non-ML

Data Science + GenAI Foundations

For freshers or non-ML backgrounds — math, ML, and GenAI fundamentals to get job-ready.

  • •Python, Stats, Linear Algebra essentials
  • •Classic ML → Transformers basics
  • •Your first RAG, LoRA, and evaluation
  • •Capstone: end-to-end GenAI project
Generative AI Program Overview
Generative AI Course is a future-ready engineering track that teaches you to build production-grade AI systems with cutting-edge techniques like MoE (Mixture of Experts), Reinforcement Learning from Human Feedback (RLHF), Speculative Decoding, and context-window optimization using LLaMA 3.2 and DeepSeek-V2.

You’ll explore every major building block of modern Generative AI systems—from LLMs and Vision-Language Models (VLMs) to fine-tuning strategies like LoRA and QLoRA. You’ll implement RAG pipelines with multi-vector and hybrid search using FAISS, Qdrant, Pinecone, Weaviate, and Neo4j.

The course emphasizes system-level thinking: work with LangChain, FastAPI, Gradio, vLLM, TGI, and optimize serving with KV cache and parallel decoding.

We also cover multi-agent orchestration, retrieval strategies, and cost-control optimizations. Our in-house MCP Framework helps systematize prompt, memory, and planning in reusable agentic patterns.

By the end, you’ll be interview-ready for advanced roles like GenAI Engineer, AI Research Developer, or LLMOps Specialist—capable of designing apps like DeepSeek or ChatGPT with memory from scratch.

Latest Gen AI Models & Technical Depth You’ll Master

Go beyond APIs — this course dives into the architectures, training methods, and serving pipelines behind the most advanced Generative AI models in 2025.

Large Language Models (LLMs)

  • LLaMA 3 / 4 – scaling with large context windows
  • DeepSeek – efficiency + multi-billion parameter training
  • Mistral – lightweight high-performance LLMs
  • Gemini – multimodal reasoning + tool use

Covers fine-tuning (LoRA/QLoRA), alignment (RLHF, DPO, ORPO), and serving (vLLM, KV cache, speculative decoding, MoE).

Vision-Language Models (VLMs)

  • SeamlessM4T – multilingual, multimodal translation
  • Kosmos-2 – grounded multimodal reasoning
  • Qwen-VL – open-source VLM for images + text

Understand multimodal embeddings, fusion layers, and build real cross-modal apps (text, vision, speech).

Diffusion & Generative Media

  • Stable Diffusion XL – advanced text-to-image
  • AnimateDiff – text-to-video & animation
  • Runway Gen-3 / Pika Labs – creative pipelines

Dive into denoising diffusion, latent tricks, control nets, and deploy image/video pipelines.

Coding & Specialized Models

  • CodeLLaMA 70B – code-focused LLM
  • StarCoder2 – structured code generation
  • DeepSeek-Coder – optimized for reasoning in coding

Learn transformer variations for code, fine-tune for domain copilots, and real developer AI assistants.

Top Skills You’ll Gain in the Generative AI Course

Python & Advanced Math for AI
Transformer Architectures (BERT → LLaMA 4, DeepSeek)
Vision-Language Models (Qwen-VL, Kosmos-2, SeamlessM4T)
Diffusion & Generative Media (Stable Diffusion XL, AnimateDiff, Runway Gen-3)
Large Language Models (LLMs) & SLMs
Model Fine-Tuning (LoRA, QLoRA, PEFT)
Reinforcement Learning (RLHF, DPO, ORPO)
Generative Adversarial Networks (GANs)
Multimodal AI (Text + Image + Video + Speech)
Retrieval-Augmented Generation (Hybrid RAG, Fusion RAG, Graph-RAG)
Vector Databases (Pinecone, FAISS, ChromaDB, Qdrant, Neo4j)
Serving & Infra (vLLM, TGI, KV Cache, Speculative Decoding, MoE)
Agentic AI & MCP Framework
AI Observability & Token-Level Debugging
Enterprise AI Deployment with Docker & Kubernetes

Generative AI Tools & Frameworks You’ll Master

PyTorch
Flexible Framework for Deep Learning

PyTorch is one of the most popular frameworks for developing deep learning models. Its dynamic nature makes it popular for research and production, helping users build neural networks for various applications like image recognition and NLP.

PyTorch

AI Architectures & Fine-Tuning (2025)

LLMs, VLMs, Diffusion, Multimodality, and RAG — with hands-on fine-tuning and deployment.

LLaMA 4 illustration

LLaMA 4

What it is

Meta’s latest LLM with huge context windows and stronger long-form reasoning/translation.

Fine-tuning

LoRA/QLoRA, RAG-aware adapters; faster convergence with less compute; multilingual finetunes.

Applications

Enterprise copilots, multilingual assistants, research agents.

DeepSeek illustration

DeepSeek

What it is

Open models optimized for efficiency and context scaling across billions of tokens.

Fine-tuning

Great for Hybrid RAG copilots and domain agents; strong retrieval grounding + high accuracy.

Applications

Search, enterprise knowledge bots, multi-agent systems.

Diffusion Models illustration

Diffusion Models

What it is

SDXL, AnimateDiff, Runway Gen-3 — state-of-the-art text-to-image/video generation.

Fine-tuning

LoRA + ControlNet for style, layout, and motion control; efficient fine-tunes.

Applications

Creative AI, media gen pipelines, design automation.

Techniques Explained for Generative AI

LoRA / QLoRA diagram

LoRA / QLoRA

What it is

Lightweight fine-tuning with far fewer trainable parameters.

How it works

Injects low-rank adapters in target layers; avoids full model retrain.

Why it matters

Enables fine-tuning on <24GB VRAM GPUs; rapid iteration for teams.

RAG & Hybrid RAG diagram

RAG & Hybrid RAG

What it is

Ground LLMs with vector DBs + BM25 + metadata filters + re-ranking.

How it works

Retriever → ranker → generator loop with citations & grounding checks.

Why it matters

Enterprise-ready QA, legal/healthcare assistants, document copilots.

RLHF, DPO, ORPO diagram

RLHF, DPO, ORPO

What it is

Alignment methods that steer models toward human-preferred behavior.

How it works

Preference optimization / policy learning atop pre-trained LLMs.

Why it matters

Safer, controllable outputs; critical for production copilots.

Serving & Infrastructure (Production)

  • vLLM & TGI — high-throughput serving with KV-cache
  • Speculative decoding — draft models for latency cuts
  • MoE routing — efficient expert utilization
  • Tracing & eval — LangSmith/LangFuse, RAGAS/DeepEval
  • Deploy — FastAPI, Docker, Kubernetes, CI/CD
  • SLMs on edge — offline & on-device copilots
Enquire Now : +91 96914 40998

Generative AI Mastery Roadmap

Foundation Refresher

Reboot your AI mindset with core foundations: • Python for AI: Data structures, OOP, NumPy, Pandas • Math for ML: Vectors, matrices, dot product, eigenvalues • Probability & Stats: Bayes, Gaussian, mean/variance • ML Basics: Linear & Logistic Regression, SVM, Decision Trees • Toolkits: Scikit-learn, Matplotlib, Seaborn

Neural Network Essentials

Understand the DNA of deep learning systems: • Perceptron, Activation & Loss Functions • Gradient Descent: Vanilla, SGD, Adam, RMSProp • Neural Networks & Backpropagation • CNNs: Conv, Pooling, ResNet, EfficientNet • RNNs, GRU, LSTM – Sequence Modeling • GNNs Intro: Graph convolutions & node classification • Framework: PyTorch + TensorBoard

Applied Deep Learning

Use DL to solve real-world problems: • Vision: Image classification, object detection (YOLOv8), segmentation (U-Net) • NLP: Text classification, NER, summarization • Audio: Speech-to-text, audio tagging • Projects: Face mask detector, sentiment classifier, voice command recognizer • Deploy using ONNX, TorchScript

Generative AI Fundamentals

Build creative AI with generative architectures: • Latent Representations & Sampling • Autoencoders: Regular & Variational • GANs: Vanilla, StyleGAN2, CycleGAN • Diffusion Models: DDPM, Latent Diffusion, ControlNet • Prompt-to-Image & Prompt2Prompt • Tools: Hugging Face Diffusers, ComfyUI, AUTOMATIC1111

LLMs Demystified

Decode large language models: • Transformer Architecture: Attention, Multi-head, Feedforward • Positional Encoding: Sinusoidal, ROPE, ALiBi • GPT, BERT, T5, BART — comparative breakdown • Tokenization: BPE, SentencePiece • Sampling: Greedy, Beam, Top-k, Top-p • Model internals: KV Cache, LayerNorm, Residual Paths • Libraries: HuggingFace Transformers, OpenAI API, vLLM

GenAI for Vision

Unlock vision-language synergy: • VLMs: CLIP, BLIP-2, Flamingo, LLaVA, Gemini • Text-to-Image: Stable Diffusion XL, DALL·E 3, MidJourney • Segmentation + Diffusion: ControlNet, SAM, DragGAN • ViT, DETR architectures • Applications: Image captioning, VQA, visual search • Tools: OpenCLIP, Diffusers, Gradio, Streamlit

Multimodal AI Architectures

Create systems that see, speak, understand: • Fusion Types: Early, Cross-Attention, Late • Modalities: Text, Image, Audio, Video • Models: Kosmos-2, GPT-4V, MM-ReAct • Use Cases: Interactive tutors, enterprise bots, medical diagnostics • Tools: HuggingFace, LangChain Multi-Modal, LLaVA+LangGraph

Finetuning GenAI Models

Customize powerful models: • Techniques: SFT, LoRA, QLoRA, DAPT, PEFT • RLHF Stack: PPO → DPO → RLAIF • Prompt vs. Parameter Tuning • Evaluation: BLEU, ROUGE, Perplexity • Libraries: Axolotl, PEFT, TRL • Real-World: Finetune LLaMA, Mistral, Phi

Retrieval-Augmented Generation (RAG)

Supercharge LLMs with external knowledge: • Chunking: RecursiveTextSplitter, Semantic Chunking • Embeddings: OpenAI, HuggingFace, Cohere, Instructor • Vector DBs: FAISS, Qdrant, Pinecone, Weaviate • Tools: LangChain, LlamaIndex, Haystack • Advanced: Hybrid Retrieval, Graph-RAG, Rerankers • Use Cases: Legal/Medical assistants, custom chatbots

Model Quantization & Serving

Speed & scale GenAI: • Quantization: INT8, GPTQ, AWQ, SmoothQuant • Compression & Distillation: DistilBERT, TinyLLaMA • Fast Inference: vLLM, DeepSpeed-MII, FasterTransformer • Serving: Triton, FastAPI, BentoML • Hosting: HF Hub, Replicate, Modal Labs

Reasoning & RLHF

Infuse logic & alignment: • Chain-of-Thought, ReAct, Toolformer • Function Calling: LangChain, OpenAI tools • Memory & Context Injection • RLHF: PPO, DPO, RLAIF • Eval: TruthfulQA, MT-Bench, AlpacaEval

Agentic AI Introduction

Build autonomous AI agents: • Agent Types: Reflex, Goal-based, Utility, Learning • SDKs: AutoGen, CrewAI, LangGraph, Semantic Kernel, SuperAgent • Core Loops: Sense → Think → Act • Multi-Agent: DAG, Role-based, chat loops • Use Cases: Sales assistants, debugging agents, meeting orchestrators

Generative AI Course Curriculam

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Industry-Trusted Generative AI Certificate

On completing the Generative AI Certification Course, you’ll receive an industry-grade certificate — validating your skills in building and deploying LLMs, VLMs, Diffusion pipelines, Hybrid RAG, and alignment methods like RLHF / DPO. This cert signals you can ship production-ready GenAI systems with vLLM/TGI, KV-cache, and modern observability.

SCHOOLOFCOREAI

CERTIFICATE

OF ACHIEVEMENT
This certificate is presented to
Shweta Sharma

Has successfully mastered the Generative AI Certification Course and demonstrated proficiency in LLMs, VLMs, Diffusion, Hybrid RAG, and RLHF/DPO — meeting the competencies required by industry.

ADVANCEDCERTIFIED
Aishwarya Pandey
Founder & CEO
DD/MM/YY
SCAI-GENAI-000987

How Our Generative AI Program Outperforms Other

Updated for 2025: MCP (Model Context Protocol), Hybrid RAG patterns, and production-grade evaluation.

Comparison of our Generative AI program vs other courses
FeatureOur ProgramOther Courses
LLMs & RAG Modules✔ End-to-end RAG: retrieval setup, tool use, function calling, grounding & citations.✘ Prompt-only demos; little grounding or retrieval planning.
MCP (Model Context Protocol) & Tool Interop✔ Portable tool adapters, unified context, and vendor-neutral integration patterns.✘ No MCP; tight coupling to a single SDK/provider.
Hybrid RAG & Re-Ranking✔ BM25 + dense + metadata filters with re-ranking, multi-hop queries, and eval.✘ Single-vector lookups; weak grounding and quality checks.
Project-Based Curriculum✔ 20+ projects: fine-tuning, multimodal apps, domain RAG, internal copilots.✘ 1–2 assignments; no real deployment.
Deployment & Scaling✔ FastAPI, Docker, Kubernetes; vector DBs; CI/CD and autoscale practices.✘ Notebook demos; no infra readiness.
Tooling Ecosystem Mastery✔ Hugging Face, Diffusers, OpenAI SDKs, Pinecone, Chroma, FAISS.✘ Surface-level API coverage.
Evaluation, Tracing & Guardrails✔ RAGAS/DeepEval, LangSmith/LangFuse, policy tests, PII filters, regression suites.✘ Minimal evaluation; no traceability/safety gating.
Live Mentorship & Expert Sessions✔ Weekly live classes, 1:1 mentorship, office hours with AI engineers.✘ Pre-recorded only; no technical mentorship.
Placement Support & Hiring Network✔ Hiring partners, referrals, industry-recognized certificate.✘ Completion certificate only.

Generative AI Course Fees

India’s most comprehensive Generative AI Engineering Program with one-time pricing, lifetime access, and complete placement support.
₹10,000 OFF — Limited Time
One-time Payment
₹74,999
₹64,999
EMI options available
Flat ₹64,999 — No hidden charges. Includes full placement support & Generative AI certification.

Original Price

₹74,999

Instant Discount

− ₹10,000

You Pay

₹64,999

Included Benefits:

  • Hands-on with LLMs, VLMs, diffusion & coding models; MoE, RLHF, speculative decoding.
  • RAG pipelines (hybrid & multi-vector) with FAISS, Qdrant, Pinecone, Weaviate, Neo4j.
  • Serving & optimization using vLLM / TGI, KV cache, parallel decoding, scalable APIs.
  • Fine-tuning with LoRA/QLoRA; multi-agent orchestration via LangGraph / Autogen.
  • Portfolio projects + interview prep for GenAI Engineer & AI Engineer roles.
  • Lifetime access to recordings, toolkits, and future updates.
Enroll NowEnquire Now : +91 99978 00680

Generative AI Salaries & Career Opportunities (India & Global)

See where this skillset takes you. Realistic salary bands, in-demand roles, and industries hiring top GenAI engineers in 2025.

Entry (0–1 yrs, good projects)

Base
₹6–15 LPA

Intern/Junior AI/ML roles with GenAI exposure

*Ranges are indicative; vary by company, city, skills & equity/bonus.

Mid (2–5 yrs, GenAI-focused)

Base
₹22–45 LPA

RAG/serving/finetuning in production

*Ranges are indicative; vary by company, city, skills & equity/bonus.

Senior (5–8 yrs)

Base
₹50–80 LPA

Lead GenAI engineer, LLMOps, platform ownership

*Ranges are indicative; vary by company, city, skills & equity/bonus.

Principal / Staff

Base
₹80 LPA – ₹1 Cr+

R&D, platform lead, model/infra ownership

*Ranges are indicative; vary by company, city, skills & equity/bonus.

High-Demand Job Titles

Generative AI EngineerLLMOps / Serving Engineer (vLLM, TGI)RAG Systems ArchitectApplied Research Engineer (LLM/VLM/Diffusion)AI Product/Platform EngineerMultimodal Engineer (VLMs, audio/video)Code AI Engineer (Code LLMs, IDE copilots)

Industries Hiring GenAI Talent

SaaS & CloudFinTech & BankingE-commerceHealthcareConsultingMedia & GamingDeveloper Tools
📄 Download Full Syllabus📞 Talk to a Career Advisor

Advance Your AI Career with LLMOps, MLOps & AIOps

After mastering Generative AI, take the next leap into serving, scaling, evaluating and governing AI systems for enterprise reliability.

Deploy & Pipelines

MLOps

Traditional ML pipelines, CI/CD, reproducibility, and reliable deployments.

  • •MLflow, TorchServe, Docker, Kubernetes
  • •Feature stores, versioning, model registry
  • •CI/CD, monitoring, rollback strategies
LLM Production

LLMOps

Serve and scale LLMs with evals, tracing, safety, and governance.

  • •vLLM/TGI, KV-cache, speculative decoding, MoE
  • •Tracing: LangSmith/LangFuse • Evals: RAGAS/DeepEval
  • •Guardrails, red-teaming, data governance
Full-Stack Ops

AIOps

Unified operations for ML + LLM + Agent systems at enterprise scale.

  • •MLOps + LLMOps + AgentOps integration
  • •Cost & latency SLOs, autoscaling, caching
  • •Observability & incident response for AI apps
Bundle & Save

End-to-End Mastery: GenAI → MLOps → LLMOps → AIOps

Get the full stack with a bundle discount • Limited cohort seats

What Our Learners Say

Hear real experiences from professionals who’ve completed this course

"I was part of the automation team at EY, and wanted to grow beyond rule-based systems. This Generative AI course helped me deeply understand LLMs, fine-tuning, and building agent-based AI systems. From Python fundamentals to deploying RAG pipelines, it covered everything I needed to become a certified Gen AI Engineer."
Aditi Sharma
Gen AI Engineer, EY
"I transitioned from traditional ML to Generative AI, and this course made that shift seamless. The curriculum taught me how to fine-tune models, build with LangChain and LangGraph, and deploy scalable systems using FastAPI and Kubernetes. It’s the most engineering-focused Gen AI course I’ve taken."
Ravi Patel
Deep Learning Engineer, TCS
"Coming from a data analytics background, I was looking to move into core AI engineering. This course helped me master the foundations of transformers, work with vector databases like FAISS and Pinecone, and architect RAG systems for enterprise-scale AI solutions. I now contribute to end-to-end AI systems at work."
Neha Gupta
AI Engineer, Enterprise AI Team
"This course gave me a deep understanding of transformer architectures, attention mechanisms, and diffusion models. We worked on real-world Gen AI systems — not toy projects. I now work on medical document summarization using fine-tuned LLMs and custom embedding pipelines."
Arjun Singh
Machine Learning Engineer, HealthTech
"What stood out in this Generative AI course was the attention to research-backed implementations. We worked with Hugging Face Diffusers, LoRA fine-tuning, and multimodal architectures from scratch. It helped me land a role as an AI research engineer focused on image–text models."
Sanya Mehta
AI Research Engineer, Creative AI Lab
"I joined the course to upskill from ML pipelines to LLM systems. We explored LangGraph, RAG with Pinecone, and LoRA-based fine-tuning of LLaMA. It gave me confidence to architect GenAI stacks and present those to our CTO. It’s deeply technical and thoughtfully structured."
Vikram Rao
Gen AI Engineer, Startup CTO Office

Your Questions Answered – Generative AI Course

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