Build the AI systems that power the next decade of products.
From classical ML to modern LLM apps. GPU-accelerated lab access. Built around what teams in industry actually do — not academic theory.
AI is the highest-pay vertical in tech right now
Generative AI changed everything in 24 months. Companies are scrambling to hire engineers who can ship AI features — not just train classifiers on Kaggle datasets, but build production AI systems with LLMs, RAG, agents, fine-tuning, evaluation, and cost management.
There's a massive skill gap. Online courses teach yesterday's ML (scikit-learn classification on Titanic). Engineers who can build today's AI (LangChain RAG bots, fine-tuned domain models, multi-agent systems, eval pipelines) get 30–50% pay premiums.
Our AI/ML tracks cover the full stack — classical ML for foundation, deep learning for depth, and modern GenAI/LLM tooling for shipping today. Real GPU-accelerated lab access. Real production deployment.
Is this track right for you?
Aspiring ML engineers
Want a job titled "ML Engineer"? ML A–Z + Deep Learning + a deployed model project.
Software engineers leveling up
You can code? Add GenAI + LLM skills — your salary band jumps a full level.
Researchers / academics
Want to bridge research → industry? Deep Learning + CV/NLP tracks pair perfectly.
Product engineers
Build AI features for your company. GenAI for Business + AI App Development.
AI / ML courses
Six progression-ordered tracks. From ML foundations to LLM production systems.
Machine Learning A–Z
You'll learn: Regression, classification, clustering, ensemble methods, XGBoost, model evaluation, feature engineering
Outcome: 3 deployed ML models on your portfolio
Deep Learning with PyTorch
You'll learn: CNN, RNN, transformers, GANs, training loops, mixed precision, distributed training
Outcome: Train + deploy a custom DL model end-to-end
Generative AI & LLMs
You'll learn: Prompt engineering, LangChain, RAG, fine-tuning (PEFT/LoRA), agents, evaluations, cost optimization
Outcome: Production GenAI app + custom RAG bot deployed live
Computer Vision
You'll learn: OpenCV, YOLO, segmentation, object detection, OCR, video analysis
Outcome: Real-world CV system (e.g. ANPR, vehicle counter)
Natural Language Processing
You'll learn: Embeddings, BERT, fine-tuning, NER, sentiment analysis, chatbots, modern transformers
Outcome: NLP-powered backend service in production
AI for Business / No-Code
You'll learn: ChatGPT, Claude, n8n, Zapier, Make, automation workflows, prompt patterns
Outcome: AI-augmented workflows for any team
Real AI systems you'll build
Production RAG chatbot
Build + deploy a domain-specific RAG bot — own knowledge base, hybrid retrieval, eval pipeline.
Fine-tuned domain LLM
Fine-tune Llama 3 / Mistral on a domain dataset using LoRA. Compare against baseline.
Computer vision app
Build a real CV system — license plate detection, retail object counter, or sports analytics.
Multi-agent system
CrewAI / LangGraph-style multi-agent workflow solving a real problem.
Tools you'll work with
What makes our AI track market-aligned
Production-first, not research
We teach what teams ship — RAG, fine-tuning, eval — not just theory. Research is one path; production is another.
GPU lab access included
Free access to GPU instances for training. No "use Google Colab and hope for the best".
Real model deployment
Deploy your models on AWS Sagemaker / HF Spaces / Modal. Not just train — ship.
LLM cost engineering
We teach prompt caching, token optimization, model fallback — the economics that production AI requires.
Roles this track prepares you for
ML Engineer
₹12–35 LPA
AI Engineer (GenAI)
₹14–40 LPA
Computer Vision Engineer
₹10–30 LPA
NLP Engineer
₹10–30 LPA
MLOps Engineer
₹14–36 LPA
AI Research Engineer
₹18–50 LPA
Frequently asked questions
Is GenAI a fad or a real career?
GenAI is to AI what cloud was to IT in 2010 — a permanent shift. The roles will evolve, the salary premium will compress over time, but the demand is real and growing for 5+ years minimum.
Do I need a Master's for AI roles?
Not for ML Engineer / AI Engineer roles. Strong portfolio + production deployments matter more. Masters helps for Research Engineer / scientist roles only.
Will AI replace AI engineers?
AI augments AI engineers. The work moves up — from training models to evaluating, deploying, debugging production AI systems. The base skill still matters.
Which framework — PyTorch or TensorFlow?
Industry has shifted to PyTorch. Our Deep Learning track is PyTorch-first. TensorFlow only if your specific target company uses it.
Start the AI / ML track
Highest-pay tech vertical. Real production AI systems. Apply now.
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