AI-First Product Building & Management
Edwagon
Remote
- 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.
- 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.
- 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.
- 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.