📘 Program Brochure

Advanced Certification in GenAI & Multi-Agent Systems

A 6–7 month weekend-live bootcamp engineered to turn developers into production-ready GenAI engineers — with placement assistance and a Pay After Placement option.

Duration

6–7 Months

Format

Weekend Live

Mode

100% Online

Modules

6 · 24 Weeks

See Fee Structure Download PDF Brochure

0–7 mo

Duration

0 modules

Curriculum blocks

Capstone projects

Hiring partners

What you'll achieve

Outcomes that change your career

By the time you graduate, you'll have shipped products, built credentials and unlocked GenAI Engineer roles.

Ship 10+ GenAI products

From prompt apps to multi-agent systems and a full capstone — all on your GitHub.

Master the modern AI stack

OpenAI, Claude, LangChain, LangGraph, RAG, vector DBs, FastAPI, Docker, LoRA & QLoRA.

Land GenAI roles

₹15L–₹35L LPA opportunities at Microsoft, Google, Nvidia and 927+ hiring partners.

IBM-certified credentials

Earn an official IBM certificate that recruiters search for on LinkedIn.

Who it's for

Built for the next generation of AI builders

If any of these sound like you, this program is engineered for your trajectory.

Software Developers

Backend / full-stack engineers ready to add LLMs, RAG and agents to their stack.

Final-Year Students & Freshers

CS / IT / engineering students aiming for AI Engineer roles right out of college.

Data / ML Engineers

Analysts and ML practitioners pivoting from traditional ML to LLMs & agentic AI.

Career Switchers

Working professionals moving into the highest-paying engineering specialisation of 2026.

What you'll learn

Skills mapped to real GenAI roles

Design production-grade prompts (ReAct, role-based, self-consistency)

Integrate OpenAI, Anthropic, Gemini & Mistral APIs securely

Build RAG pipelines with LangChain, LlamaIndex & vector DBs

Architect autonomous AI agents and multi-agent teams

Ship LLM apps with FastAPI, Docker, CI/CD & monitoring

Fine-tune open models with LoRA / QLoRA without a GPU farm

Implement evals, guardrails & cost optimisation

Deploy capstone GenAI products with public demos

Curriculum

A masterclass in GenAI

A meticulously sequenced path from prompt-curious to production-ready GenAI engineer.

01Weeks 1–4

Prompt Engineering & API Integration

Move from casual ChatGPT user to building production apps that talk to LLMs via APIs.

AI vs ML vs DL — think like an AI engineer

Anatomy of a prompt (Role, Instruction, Input, Output)

Role-based prompting & expert personas

ReAct & self-consistency for reliable outputs

Open vs closed-source models (OpenAI, Claude, Mistral, HF)

OpenAI / Anthropic / Gemini API integration

Key management, rate limits & error handling

Prompt evaluation & iterative optimisation

Ethics, safety & responsible AI

OpenAI Anthropic Claude Gemini HuggingFace Postman

OutcomeShip production-grade LLM apps with secure, evaluated prompts.

02Weeks 5–8

NLP & Retrieval-Augmented Generation

Ground LLMs in your own data with embeddings, semantic search and end-to-end RAG.

NLP fundamentals: tokenisation & preprocessing

NER, POS tagging & text representation

Embeddings & semantic search

Chunking strategies for long documents

Vector databases (Pinecone, FAISS, pgvector)

End-to-end RAG pipelines with LangChain & LlamaIndex

Advanced RAG: multi-hop, re-ranking, self-retrieval

Reducing hallucinations & RAG evaluation

LangChain LlamaIndex Pinecone FAISS pgvector Chroma Weaviate

OutcomeBuild a RAG product that answers from your own data, with citations.

03Weeks 9–12

AI Agents & Multi-Agent Systems

Stop prompting AI — start building AI workers that plan, use tools and collaborate.

What is an AI agent — reflex, goal-based, utility

Design patterns: ReAct, tool-based, compiler agents

LangChain, LangGraph & AutoGen frameworks

Tool usage: search, calendar, APIs

Multi-agent systems & coordination

Hierarchical agents & consensus

Planning, reasoning & multi-step workflows

MARL — reinforcement learning across agents

Latency & cost optimisation

Agents LangChain LangGraph AutoGen Tavily OpenAI

OutcomeDeploy autonomous multi-agent systems that complete real workflows.

04Weeks 13–16

Productionising GenAI & LLMOps

Wrap your GenAI systems in clean APIs, ship a UI, dockerise and monitor in prod.

Web API basics + FastAPI & Pydantic

Auto-generated docs with Swagger UI

Modular system design (memory, RAG, UI)

Frontend integration with Gradio / Streamlit

Dockerisation of GenAI services

CI/CD with GitHub Actions

Deploy on Render, Railway, Replit

Security, env management & abuse prevention

LLMOps: cost, perf, version tracking

Tracing with Langfuse, LangGraph & MLflow

FastAPI Docker Render Railway Gradio Streamlit GitHub Actions LangFuse MLflow

OutcomeLaunch a public, monitored GenAI product end-to-end.

05Weeks 17–20Optional · Deep dive

Neural Networks & Transformers

Go from using AI to understanding it — perceptrons, backprop, attention & transformers.

Linear & logistic regression refresher

PyTorch / TensorFlow / Keras workflows

Perceptron, MLP, loss & activation functions

Backpropagation & gradient descent

CNNs, RNNs & LSTMs

Why transformers replaced RNNs

Attention mechanism deep dive

Transformer architecture (encoder/decoder)

Pretraining, finetuning & transfer learning

Visualising attention with BertViz

PyTorch TensorFlow Transformers HuggingFace

OutcomeExplain — and modify — what's actually happening inside an LLM.

06Weeks 21–24Optional · Deep dive

Fine-Tuning LLMs + Capstone

Train your own model with PEFT, then ship a capstone combining RAG + Agents + Fine-tuning.

When to fine-tune vs use RAG

SLM vs LLM — choosing the right model

LLaMA, Mistral & Phi model families

Dataset prep, cleaning & labelling

Alpaca & DPO open-source datasets

HuggingFace Trainer pipeline

PEFT — LoRA & QLoRA on consumer GPUs

Quantisation & fast inference

Eval metrics: F1, BLEU, custom evals

Cost estimation & capstone product launch

LLaMA Mistral Phi PEFT LoRA QLoRA Weights & Biases Capstone

OutcomeShip a fine-tuned, domain-specific model as your capstone product.

Projects you'll ship

Real GenAI products, not toy demos

A curated slate of projects modelled on what's actually trending in production GenAI today.

Intermediate

Capstone-grade

Talk-to-your-Docs RAG

A multi-tenant RAG app that ingests PDFs, contracts and websites — answers with citations and refuses out-of-context questions.

LangChain Pinecone OpenAI FastAPI Streamlit

Advanced

Multi-agent

Multi-Agent SDR

An autonomous outbound sales rep — researches a lead, drafts a personalised cold email, books a meeting on your calendar.

LangGraph AutoGen Tavily Claude FastAPI

Intermediate

Search × LLM

Perplexity-style Answer Engine

Live web search with grounded citations and follow-up reasoning — your own Perplexity clone deployed end-to-end.

Tavily LangChain OpenAI Gradio Docker

Intermediate

Vertical RAG

Medical Report Analyser

Upload a lab report, get a plain-English explanation with risk flags and structured JSON for downstream EMRs.

Claude LlamaIndex pgvector FastAPI

Advanced

Multimodal

Voice-First Shopping Agent

Speak naturally, get product recommendations, add to cart and checkout — agent calls real e-commerce APIs.

OpenAI LangGraph FastAPI Streamlit

Intermediate

Structured output

Legal Clause Extractor

Extracts indemnity, termination and liability clauses from contracts into a clean schema with confidence scores.

Claude LangChain FAISS FastAPI

Beginner

Image gen

AI Storyboard Generator

Turn a one-line idea into a 6-panel storyboard with consistent characters using diffusion + LLM scene planning.

Gemini OpenAI Gradio

Advanced

Speech × LLM

Meeting Intelligence Bot

Joins meetings, transcribes speakers, extracts action items and posts a follow-up summary to Slack — all autonomously.

OpenAI LangChain FastAPI Docker

See full project list in the brochure

Placement assistance

Hired, not just certified

Every cohort moves through the same proven placement engine that delivered 1,437+ placements.

927+ hiring partners

Active GenAI roles at Microsoft, Google, Nvidia and top AI startups.

Assured interviews

Skill-matched interview drives until you're placed.

1:1 career mentor

Working GenAI engineer guiding your job search end-to-end.

Pay After Placement

Pay only after you land a qualifying job offer (Pro track).

Alumni in the wild

Engineers shipping real GenAI right now

A snapshot of where our recent grads landed — and what got them hired.

Devansh T.

Java Dev · 5 yrs

Now at

Google

Lead GenAI Engineer

CTC

₹35L

per annum

The LangGraph + multi-agent module was the unlock. I built my capstone around it and that's exactly what I shipped on the job in week 2.

Ishita M.

Backend Dev · 3 yrs

Now at

Nvidia

GenAI Engineer

CTC

₹34L

per annum

Went from never having touched an LLM API to fine-tuning Mistral with QLoRA. Mock interviews were brutally honest — exactly what I needed.

Aarav S.

Final-year B.Tech

Now at

Microsoft

AI Engineer

CTC

₹28L

per annum

Got placed before graduating. The capstone RAG product is still the first thing recruiters ask about on every call.

Sneha K.

Full-stack Dev

Now at

Razorpay

AI Product Engineer

CTC

₹26L

per annum

What I loved: every concept ended in a deployed app. No 'we'll cover deployment later' — it's baked into every module.

Anika R.

Data Scientist

Now at

Salesforce

RAG Systems Engineer

CTC

₹24L

per annum

Pivoting from classic ML to LLM systems felt impossible alone. The mentor sessions and the agent module made it click.

Rohan P.

Data Analyst

Now at

Adobe

ML / LLM Engineer

CTC

₹22L

per annum

Three rounds of interviews, all on systems I'd literally built in class. The hiring partner pipeline is the real deal.

Career tools

An AI career toolkit bundled in

Built by Skillians, used by every cohort — included with both Basic and Pro tracks.

Resume Optimiser

ATS-aware resume builder tuned for AI Engineer roles.

AI Mock Interview

Live AI coach simulating real GenAI engineering interviews.

LinkedIn Optimiser

Recruiter-ready profile rewrite with GenAI keywords.

GitHub Portfolio Reviews

Engineer-led reviews of your projects & READMEs.

Fee structure

Invest in outcomes

Two tracks. Pick Pro for IBM certification and dedicated placement assistance.

All prices inclusive — no hidden fees

Gen AI Basic

Get started with the core Gen AI engineering stack.

₹35,000+ 18% GST

Total payable: ₹41,300

Full Gen AI core curriculum
Live instructor-led classes
Capstone & hands-on projects
Community access & doubt support
Course completion certificate
Limited usage of AI career tools
Standard placement support

Enroll in Basic

Recommended

Gen AI Pro

In association with IBM

In association with IBM — built for serious career outcomes.

₹59,000+ 18% GST

Total payable: ₹70,000

Everything in Gen AI Basic
Official IBM Certification
Dedicated 1:1 career assistance
Unlimited usage of all AI career tools
Premium Naukri, Hirist & Cut-short accounts
Mock interviews & resume reviews
Priority placement referrals
Lifetime cohort & alumni network

Claim your Pro seat

Tech stack

The exact stack employers want

The same APIs, frameworks and infra used by GenAI teams at top product companies.

OpenAI Anthropic Claude Gemini Mistral LLaMA Phi HuggingFace Transformers LangChain LangGraph LangFuse LlamaIndex AutoGen Pinecone FAISS pgvector Chroma OpenAI Anthropic Claude Gemini Mistral LLaMA Phi HuggingFace Transformers LangChain LangGraph LangFuse LlamaIndex AutoGen Pinecone FAISS pgvector Chroma

Weaviate PEFT LoRA QLoRA FastAPI Docker GitHub Actions Render Railway Replit Gradio Streamlit Postman MLflow Weights & Biases Tavily PyTorch TensorFlow Weaviate PEFT LoRA QLoRA FastAPI Docker GitHub Actions Render Railway Replit Gradio Streamlit Postman MLflow Weights & Biases Tavily PyTorch TensorFlow

Hiring partners