Beginner · Live Online · 3 days

AI Developer Bootcamp

Build 3 real AI projects in 3 days — the fundamentals track, open to students and working developers alike.

Reserve my seat See the syllabus

Live, hands-onYour own GitHub repoVerifiable certificate

Duration3 days · 4 hrs/day · 12 hours live

LevelBeginner

FormatLive Online

Next batchDates announced soon

Fee₹999

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Why this bootcamp

Every student says they "know AI" because they use ChatGPT. Recruiters have stopped being impressed by that.

What still gets attention is a working application you built yourself, with the code public and the reasoning you can explain in an interview. That is what these three days produce.

You will build three AI projects — an AI application, a knowledge assistant that answers questions over real documents, and a deployed AI agent. You leave with a GitHub repository, architecture diagrams you can talk through, resume bullet points, and interview questions to prepare against.

No prior AI experience needed. You do need to be comfortable writing basic code in some language.

What you'll build

  YOU, AFTER 3 DAYS
         |
         +----> GitHub repository (3 projects, clean READMEs)
         |
         +----> Deployed AI agent (a live URL you can share)
         |
         +----> Architecture diagrams you can explain
         |
         +----> Resume bullets + LinkedIn project write-up
         |
         +----> Verifiable certificate
         |
         v
   INTERVIEW-READY

Syllabus

Day by day

Day 1

Build your first GenAI application

How AI applications actually work: tokens, context, embeddings, retrieval — explained without the maths
Why 'I use ChatGPT' is not an AI skill, and what is
Set up Python, VS Code, GitHub and your first LLM API key
Get structured, reliable output out of a model instead of a wall of text
Project 1: an AI application with a real interface, running on your machine
Push it to GitHub properly — README, structure, commit history that looks professional

Day 2

Build an AI knowledge assistant (RAG)

Why a model cannot answer questions about your documents, and how retrieval fixes that
Embeddings, chunking and vector databases — hands-on, not theory
Retrieval quality: why your first RAG app gives bad answers and how to improve it
Citations and grounding, so the answer can be checked
Project 2: upload your own notes, syllabus or documents and query them
Draw the architecture diagram for what you just built

Day 3

Build and deploy an AI agent

Agents versus chatbots: what changes when the model can take actions
Tool calling — letting your AI search, fetch and calculate
Multi-step reasoning and where agents typically fail
Deploy to the cloud so you have a link, not just localhost
Project 3: a deployed AI agent anyone can open in a browser
AI Product Challenge: team demos, 5 minutes each, awards for best product and best engineering
Portfolio session: resume bullets, LinkedIn write-up, and the interview questions you will be asked

Topics covered

PythonLLM APIsPrompt designStructured outputFastAPIEmbeddingsVector databasesRAGAI agentsTool callingGitHubDeploymentPortfolio buildingInterview prep

Outcomes

What you'll be able to do

Build and run an AI application end to end, on your own machine
Build a RAG system that answers questions over documents you provide
Build and deploy an AI agent that takes real actions
Explain your architecture in an interview without hand-waving
Publish three projects on GitHub with professional READMEs
Write resume and LinkedIn entries that describe real engineering work
Know what to learn next, and in what order

Before

After

I use ChatGPT for assignments

→

I build applications that use AI as a component

My resume has coursework and a mini-project

→

My resume has three AI projects with public code and a live demo

I freeze when asked 'explain your project'

→

I can walk through my architecture and justify each decision

I do not know where to start with AI

→

I know what to build next and which skills matter

Who it's for

Built for people who build software

Students (BE/BTech, BCA/MCA, MTech)CS, IT, AI/DS — build something real before placement season

Final-year studentsA portfolio project you can actually defend in an interview

Freshers & career switchersA concrete, demonstrable starting point in AI

Working developers new to AIYou can code, but haven't shipped an AI application yet

Self-taught learnersYou've done the courses — now turn them into something you can show

Anyone who wants the fundamentals firstNo prior AI experience assumed, whatever your job title

This is not…

A placement guarantee — we teach skills, we do not promise jobsA ChatGPT or prompt-writing tutorialA theory course with slides and no codingA machine-learning maths courseA certificate you can add without doing the workA substitute for knowing how to program

Your instructor

Dr Ganesh

AI Product Engineer · Generative AI, AI Systems & Software Architecture

Dr Ganesh works on practical engineering problems involving generative AI, LLM applications, AI agents, RAG systems, knowledge graphs, software architecture and code intelligence.

He teaches the way the work is actually done — starting from a problem, ending at a deployed system. This workshop covers practical engineering concepts and general industry knowledge, and does not expose proprietary client or employer intellectual property.

VithuPro on LinkedIn

FAQ

Questions people ask

Do I need to know AI or machine learning already?

No. You need basic programming in any language. We build the AI concepts up from scratch.

Do I need to know Python?

It helps a lot. If you know Java, C++ or JavaScript you will keep up, but do spend an hour on Python basics beforehand.

Will this get me a job or placement?

No, and be sceptical of anyone who promises that. What this gives you is real projects, working code and the ability to discuss them — which is what interviews actually test.

I'm a working professional — is this too basic for me?

It depends on what you've built, not your years of experience. If you have never shipped an AI application, start here. If you already call LLM APIs comfortably and want architecture, RAG evaluation and production concerns, take AI Product Engineering instead.

What do I need on my laptop?

A laptop with 8GB RAM, a browser and VS Code. We send a setup guide and API key instructions before Day 1.

Are there costs beyond the fee?

LLM API usage during the workshop is covered by free tiers and credits we help you set up. Cloud deployment uses free hosting.

Will I get a certificate?

Yes — a Certificate of Completion with a verifiable link.

Are sessions recorded?

Yes, recordings are shared after each day.

Can my college host this for our students?

Yes. We run the same bootcamp on campus for departments and student chapters — use the enquiry form and mention your college.

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You should walk out able to build the thing, not just talk about it.

3 days · 4 hrs/day · 12 hours live · Live Online · ₹999

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