Learn the infrastructure discipline that keeps AI systems reliable, observable and cost-efficient once they leave the notebook.
A model that works in a notebook is not a product. Companies need engineers who can containerise models, automate retraining and deployment, monitor for drift and cost, and keep GPU spend under control — a skill set that's consistently in short supply relative to demand.
The track covers containerising and serving models, building CI/CD pipelines for ML, setting up monitoring for accuracy drift and latency, managing vector databases for retrieval systems, and controlling cloud GPU costs at scale.
DevOps and backend engineers moving into AI infrastructure, and data scientists who want to own the full path from model to production.
Indicative salary bands for the Indian market — actual offers vary with experience, company and location.
Owns the pipelines that take models reliably into production.
Designs the compute and serving layer AI systems run on.
Keeps AI services fast, available and within budget.