Build Real AI.
Ship It to Prod.
Get Hired.
Not ChatGPT wrappers. You'll build production-grade RAG pipelines, fine-tune open-source LLMs, design multi-agent workflows, deploy real AI APIs on AWS — and build a portfolio of shipped AI products in 90 days.
The 90-Day Gen AI & ML Roadmap
From Python and ML basics to deploying production RAG systems, fine-tuned LLMs, and AI agents — mentor-reviewed work every single day.
Real AI Products.
Shipped to Production.
You won't be building ChatGPT wrappers. You'll work inside a real Gen AI team — building RAG pipelines on real knowledge bases, fine-tuning LLMs on domain data, shipping AI agents that actually work in production.
What You'll Build & Ship
13 weeks of real Gen AI engineering — every module ends with a working AI app in GitHub, reviewed by a senior AI engineer.
Real AI Products You'll Actually Deploy
Real AI applications with live URLs — RAG systems, fine-tuned LLMs, and deployed agents that AI hiring managers ask you to demo.
Your Gen AI & ML Journey
From your first LLM API call to a deployed multi-agent AI system — every week ends with a working AI product pushed to GitHub.
We Don't Stop Till
You're a Hired AI Engineer
Gen AI Engineer is the hottest job title in India right now. Our placement team connects your portfolio of shipped AI products directly with AI teams, startups, and product companies hiring now.
From Beginner to AI Engineer
Real Gen AI alumni. Real AI products they shipped. Real offers that launched their AI careers.
The Infosys AI CoE interviewer asked me to design a RAG system on a whiteboard. I had already built exactly that — chunking strategy, re-ranking, RAGAS evaluation. I walked through the real architecture I had shipped, not a textbook answer. Offer came the same day.
When Accenture AI asked about fine-tuning trade-offs, I opened my HuggingFace model card and walked them through the QLoRA training run — why I chose Llama 3, the dataset prep, the eval metrics. Every answer had a live link. They said it was unlike any Gen AI interview they had done that year.
I switched from a non-tech job to AI engineering at 27. The RAG system I built and deployed was what Wipro AI could not believe from a career-switcher. That live URL did all the talking.
The multi-agent CrewAI system I built surprised Google most. Three agents collaborating to research, write, and edit reports — it showed agentic design intuition. My interviewer said it was the first time a fresher had shown them a real deployed agentic AI system they had built themselves.
Deploying my Spring Boot app to AWS ECS for the first time during the internship felt impossible. By week 10, I was doing it automatically as part of the CI/CD pipeline. HCL asked me about AWS deployment in my interview — I walked them through the exact pipeline I'd built. Offer in 2 days.
Writing 88% JUnit test coverage with Mockito and Testcontainers was the skill that surprised Capgemini most. Every other fresher they interviewed said they'd done unit testing — I could show real tests, real mocks, real integration tests running against a Docker PostgreSQL container.
Your first RAG pipeline.
Your first fine-tuned LLM.
Your first AI engineer offer.
Applications open for the next Gen AI & ML batch — only 12 seats. Free to apply. We'll call you in 30 minutes.
Free to apply. Merit-based selection. No spam, ever.