Product Engineer

Role overview Build, ship, and continuously evolve an internal agentic engineering platform that powers Expert Pod teams delivering production-ready agentic AI in six-week cycles. The work spans AI applications, backend, frontend, and data layers, with a sharp focus on measurable outcomes, reusable patterns, and enterprise-grade trust. The role also drives adoption across client delivery teams through onboarding, documentation, blueprints, and pre-built components.

Responsibilities - Design, build, and maintain the core components of an agentic engineering platform used by multiple delivery teams. - Deliver platform features across AI applications, backend services, frontend interfaces, and data layers end to end. - Maintain rigorous testing, debugging, and production monitoring practices across every component. - Drive adoption inside delivery teams through onboarding, documentation, blueprints, and pre-built components. - Capture reusable patterns from client engagements and feed them back into the platform to compound its value over time. - Track and improve platform metrics such as time-to-deployment, code reuse, and cost per AI application. - Enforce security, compliance, and responsible-AI practices across the platform.

Requirements - Strong experience building and scaling full-stack platforms spanning frontend, backend, APIs, and data, paired with a sharp product sense. - Hands-on work with AI applications, LLM orchestration frameworks, SDK development, or agentic workflows. - Experience designing reusable component libraries, SDKs, or internal developer platforms that scale across teams. - Operational rigor in production monitoring, testing frameworks, CI/CD, and cloud data infrastructure. - Clear technical writing skills with the appetite to mentor and enable delivery teams. - Agility to translate complex requirements into clean, reusable platform features.

Nice to have - Familiarity with security, compliance, and responsible-AI practices for production AI systems. - Background turning client engagement learnings into reusable platform assets.