Full Stack AIOps Engineering

What you'll build 01 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.02 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.03 Customer-research playbooks tuned for AI — discovery loops, eval frameworks, and decision rubrics that work for non-deterministic AI features.04 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. How roles evolve with AI 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. What you'll build 01 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.02 AI-native operations across workflows — observability, anomaly detection, and agentic automation to run self-healing, intelligent systems in production.03 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 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.