Data Science and AI with specialization in Robotics

What you'll build

• A perception stack that runs on a real robot — detection, tracking and depth taken out of the notebook and onto hardware during the five-day lab at TIH, IIT Palakkad.
• A smart-city traffic monitoring system — vision under real conditions: occlusion, weather, changing light and a camera that never sits still.
• An end-to-end ML pipeline — cleaning, features, training and evaluation built so it can be re-run and trusted, not demoed once.
• A voice-to-robot command simulation — speech to intent to motion, where a language model has to drive something physical that can actually get it wrong.
• An AI-native product roadmap & PRD — shipped to a real cohort review panel of senior PMs, with discovery, prioritisation, and rollout decisions defended in front of practitioners.
• A working AI-powered prototype — built with LLMs, agents, and modern PM tooling, integrated end-to-end so you ship a real product, not slides.
• Customer-research playbooks tuned for AI — discovery loops, eval frameworks, and decision rubrics that work for non-deterministic AI features.
• A capstone launch reviewed by industry — final product critiqued by senior PMs from Razorpay, Google, and LinkedIn, with one-on-one feedback recorded as proof of work.
• DevOps & Cloud Engineering — Linux to Kubernetes, CI/CD, Terraform, and AWS taught as one system for real-world deployment, with hands-on labs you'd see on the job.
• AI-native operations across workflows — observability, anomaly detection, and agentic automation to run self-healing, intelligent systems in production.
• Specialisation in AI-powered infrastructure — build and deploy AI systems using Kubeflow, KServe, LangChain, Bedrock, and modern cloud tooling, with deployment runbooks you keep.

How roles evolve with AI

• Computer Vision Engineers building the perception layer for machines that have to see and act in the physical world.
• Robotics Researchers working on motion planning, SLAM and learned control at the frontier of autonomy.
• Deep Learning & AI Engineers taking models to the edge — optimised, deployed and monitored on real hardware.
• AI Product Managers who own AI-native product surfaces end-to-end — from discovery through eval, launch, and ongoing model behavior.
• AI Product Leads driving roadmaps for LLM and agentic features at scale, partnering tightly with research and platform teams.
• AI Solutions PMs embedded with engineering on customer-facing deployments — translating ambiguous AI behavior into shippable product decisions.
• Forward Deployed Engineers who embed with customers to build and deploy AI solutions directly within real-world business workflows.
• Agent Engineers who build autonomous AI agents that can reason, act, and execute tasks across tools and workflows.
• AI Solutions Engineers who design and implement end-to-end AI systems that solve specific business problems at scale.