Shipping on a $15M Pricing Platform
An enterprise pricing and assortment platform with about $15 million in annual revenue flowing through it does not get casual changes. Everything ships through pull-request review, Jenkins CI/CD, and a QA promotion pipeline, behind feature flags. The internship was ten weeks of shipping production features inside that discipline, across multiple major releases.
01 How changes ship
02 What shipped
The feature work centered on how the platform handles imperfect input and risky rollouts: partial-success API validation, so a request with some invalid entries can succeed on the valid ones and report precisely what failed instead of failing wholesale; feature-flagged rollouts, so changes reach production dark and turn on deliberately; and UI validation displays that surface those results to the people using the platform.
03 Requirements discovery for the ML initiative
The platform's AI/ML team was developing a model to identify unhealthy inventory, and models need requirements as much as features do. The second half of the internship was discovery work for that initiative: stakeholder interviews, synthesizing process artifacts, and turning what the business actually needed into a sprint-ready user story with full acceptance criteria, a definition of done, and scoped dependencies. It was the first time I watched the gap between an ML idea and a buildable specification get closed deliberately, and that bridge role is work I want more of.
04 What was built
Production features. Shipped across multiple major releases of the pricing and assortment platform: partial-success API validation, feature-flagged rollouts, and UI validation displays.
Release discipline. Every change through PR review, Jenkins CI/CD, and the QA promotion pipeline.
ML requirements discovery. Stakeholder interviews and process-artifact synthesis producing a sprint-ready user story, with acceptance criteria, definition of done, and scoped dependencies, for the unhealthy-inventory model.