Technology Product & Platform Management
All Job Posting Locations:
Raritan, New Jersey, United States of America
We are searching for the best talent for a Lead Platform Engineer — AI Web Experience to be located in Raritan, NJ.
This person will lead the implementation of Platform/Product excellence by leveraging AI across the J&J MedTech’s Web Experience team — how designs become requirements, how requirements become code, how code is reviewed, and how quality is validated. The role is internally focused: the primary measure of this role is the efficiency gains and outcome quality of our design → requirements → code → QA process.
The organization delivers a portfolio of external-facing web properties on a composable, MACH-based stack — Contentstack CMS, Next.js applications on AWS behind Cloudflare, Algolia search, Apigee API gateway services, and the Absorb LMS supporting professional education — built and operated by four to seven delivery squads.
This is a hands-on role. The successful candidate has a strong Product mindset combined with a strong Engineering passion and builds AI agents, while leading the expectation that everyone on the team ships code. It combines direct product contribution with the technical leadership required to move a large, mixed employee and contractor organization onto AI-native delivery practices.
The role drives Product excellence and the AI capability layer that the Web Experience delivery organization builds on: the agents, shared services, developer tooling, and integration patterns.
The scope is the delivery chain itself — design, requirements, code, and QA — and the mandate is to compress cycle time and raise output quality across the end to end process rather than optimizing any one of them in isolation. In practice that means agent-assisted design-to-code, design token extraction, design fidelity validation, requirements drafting and refinement, automated code review, and test generation, each integrated into the existing toolchain rather than bolted alongside it.
The role serves as the technical counterpart to product and leadership, translating an AI-native operating model into working engineering systems. A critical objective is durability. Investments must compound through shared services, standardized interfaces, reusable agents and skills, and versioned artifacts — not accumulate as one-off automations that decay when their author moves on.
The successful candidate will operate across both the Product and Platform sides of the organization, working directly with product owners, business analysts, designers, engineers, QA, and analytics across four to seven delivery squads to raise AI fluency and drive adoption.