Lead Platform Engineer - Web Experience

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. Learn more at jnj.com As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world.  We provide an inclusive work environment where each person is considered as an individual.  At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Function: Technology Product & Platform Management Job Sub Function: Functional Engineering Job Category: Scientific/Technology All Job Posting Locations: Raritan, New Jersey, United States of America Job Description: 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. Role 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. Strategic Core 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. Requirements 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, integrated with existing Git and Jenkins CI/CD workflows, with engineering standards enforced as executable checks rather than documented convention. Lead the application of AI to accessibility conformance — automated detection, assisted remediation, regression prevention, and evidence generation — embedded in the pipeline alongside deterministic accessibility tooling, so that generated code and content meet standards by construction rather than by downstream audit. QA Lead AI-assisted test generation and maintenance against the existing automation suite, with coverage derived from acceptance criteria rather than authored separately. Build and operate the quality signal that determines whether AI-produced work advances: automated checks, evaluation harnesses, and regression gates, producing the evidence that quality governance sets thresholds against. Define how the chain behaves when AI output is wrong — rework loops, escalation to human authorship, and the review posture applied at each risk tier — and instrument failure modes so that posture can be adjusted on evidence rather than instinct. Analytics Apply AI to measurement definition and decrease the time required to g