Lead Platform Engineer - Web Experience

Johnson & Johnson • Raritan, NJ

2d • Onsite

About The Position

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.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related technical field.
• 7+ years of software engineering or product experience delivering enterprise-scale digital products, platforms, and distributed systems.
• 2+ years developing and using AI agents and orchestration platforms.
• Demonstrated experience leading large-scale platform modernization initiatives and establishing enterprise engineering standards.
• Proven track record building reusable platforms, frameworks, SDKs, and developer ecosystems rather than standalone applications.
• Experience operating within highly regulated or complex enterprise environments.
• Expert in Platform Development, including the application of software solutions to solve business problems and accelerate new product introduction.
• Demonstrable experience leading teams of employees and/or contractors, including goal setting and career development.
• Demonstrated experience designing and implementing enterprise AI ecosystems including: Large Language Model (LLM) integrations, Agent-based systems, Multi-agent architectures, Agent orchestration, Retrieval-Augmented Generation (RAG), Tool-calling frameworks, Skills-based architectures, Context engineering, AI observability, AI governance, Agent-to-Agent (A2A) workflows, Model Context Protocol (MCP) integrations.
• Deep experience delivering on a composable, MACH-based architecture, including headless CMS (Contentstack or equivalent), Next.js/React applications on AWS, CDN and edge delivery (Cloudflare or equivalent), hosted search (Algolia or equivalent), API gateway services (Apigee or equivalent), and LMS integration (Absorb or equivalent).
• Hands-on expertise with Git and Jenkins CI/CD, automated test frameworks such as Robot Framework, and platform observability tooling such as New Relic.
• Working knowledge of digital accessibility standards and the tooling used to enforce them, including automated scanning.

Nice To Haves

• Experience in MedTech, pharmaceutical, healthcare, or regulated industries
• Experience building enterprise agent platforms.
• Experience building Internal Developer Platforms (IDPs).
• Experience building reusable enterprise SDKs and framework libraries.
• Demonstrated ability to influence senior executives and enterprise architecture stakeholders.
• Experience leading through organizational transformation.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• Apply AI to measurement definition and decrease the time required to generate those reports
• Apply AI to search and generative engine optimization (SEO/GEO) efficiency
• Contribute production code directly and regularly, setting engineering standards through implementation — reference agents, working examples, and reviewed patterns that other engineers adopt — and driving adoption across four to seven delivery squads through code review and hands-on pairing.
• Provide technical direction and day-to-day leadership to contractor engineering or product resources, including goal setting, performance feedback, and capability development.
• Establish the current-state baseline for the design through QA chain, then define and hold the portfolio to metrics measuring efficiency and outcome quality against it: cycle time by stage, rework and defect escape rates, platform reuse and service adoption, release velocity, and developer productivity.

Benefits

• medical
• dental
• vision
• life insurance
• short- and long-term disability
• business accident insurance
• group legal insurance
• consolidated retirement plan (pension)
• savings plan (401(k))
• Vacation –120 hours per calendar year
• Sick time - 40 hours per calendar year
• Holiday pay, including Floating Holidays –13 days per calendar year
• Work, Personal and Family Time - up to 40 hours per calendar year
• Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
• Condolence Leave – 30 days for an immediate family member: 5 days for an extended family member
• Caregiver Leave – 10 days
• Volunteer Leave – 4 days
• Military Spouse Time-Off – 80 hours

What This Job Offers

• Job Type: Full-time
• Career Level: Senior