Senior AI Platform Architect – Enterprise-Scale

Johnson & Johnson

Horsham (PA)

Hybrid

USD 137,000 - 236,000

Full time

13 days ago

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Benefits offered by this job

J&J Flex

Job summary

Johnson & Johnson is seeking a Senior Principal Platform Engineer in a hybrid role, based in either Titusville, NJ or Horsham, PA. This leader will drive the AI Next Frontier Workbench platform, defining strategy, architecture, and governance for scalable, reusable AI-enabled services across Innovative Medicine.

The role requires 10+ years in software engineering, with strong cloud-native and AI experience, and will travel up to 10% in the NJ/PA region.

Qualifications

10+ years of software engineering delivering enterprise-scale digital products, platforms and distributed systems.
7+ years as lead architect, principal engineer, or engineering lead for platform architecture and strategy.
2+ years developing and using AI agents and orchestration platforms.
Experience leading large-scale platform modernization and enterprise engineering standards.
Proven track record building reusable platforms, frameworks, SDKs, and developer ecosystems.
Experience in highly regulated or complex enterprise environments.
Hands-on cloud-native development using TypeScript, Node.js, NestJS, React, Next.js, GraphQL, REST APIs, SSR/CSR/SSG.
Deep knowledge of AWS services (API Gateway, Lambda, EKS, EC2, S3, DynamoDB, etc.).
Experience designing enterprise AI ecosystems including LLM integrations, agent-based systems, and retrieval-augmented generation.
Hands-on with Git, Jenkins, Terraform, Kubernetes, Docker, and IaC; platform observability.
Knowledge of XENA platform engineering standards and AI-enabled SDLC capabilities.

Responsibilities

Define and evolve the technical architecture for the AINF Workbench platform.
Set engineering strategy for shared services and gateway/runtime capabilities.
Build frameworks, SDKs, APIs, and developer platforms for adoption across pods and use cases.
Lead development of reusable agent frameworks, skills, and integration strategies.
Establish enterprise standards for MCP, A2A, and A2UI interoperability.
Create a shared catalog of AI services, tools, agents, and business capabilities.
Partner with AI Core teams to implement governed agent execution and observability.
Ensure Workbench integrates with the broader enterprise tech ecosystem via APIs and shared services.
Define engineering quality metrics for reuse, reliability, and velocity.
Build an organization to support long-term AINF roadmap including AI channels and enterprise adoption.