Lead Platform Engineer - Web Experience

About the role

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

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.

Location: Raritan, New Jersey, United States

Responsibilities

• Design Guide implementation of the agent capability that turns design system output into production-ready implementation — design token extraction, component generation, and page assembly against the Next.js component library and Contentstack content models.
• Build automated design fidelity validation comparing implemented output against design intent, backed by visual regression as deterministic signal, so that generated work can be trusted without manual pixel review.
• Partner with the design system team to make the design system machine-consumable, advising on the structure, metadata, and token conventions that AI generation depends on. This role does not own the design system; it is accountable for how effectively the pipeline consumes it.
• Apply AI to requirements drafting, refinement, and acceptance criteria generation from design intent and product input, and establish automated requirement-to-test traceability so that the delivery chain remains auditable end to end.
• Code Lead agent-assisted implementation and automated code review