Manager, GenAI AWS Platform Engineering

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: Intelligent Automation Engineering Job Category: Scientific/Technology All Job Posting Locations: Raritan, New Jersey, United States of America Job Description: J&J is currently seeking a Manager, GenAI AWS Platform Engineering for their Technology Services team based in Raritan, NJ. The Manager, GenAI AWS Platform Engineering leads the modernization of Johnson & Johnson's enterprise Generative AI platform on Amazon Web Services. The primary objective of this role is optimize the governed consumption of GenAI/Agentic AI capabilities on the AWS cloud as well as re-platforming: moving GenAI capabilities off bespoke, custom-built infrastructure and onto AWS cloud-native services. You are the accountable engineering leader for that transition — deciding what is replaced by an AWS-native service, what is retired, what must remain custom and why — and for engineering the AWS foundation the modernized platform runs on: Amazon Bedrock, the landing zone and multi-account architecture on AWS Organizations and Control Tower, and the networking, IAM, VPC and PrivateLink design that keeps GenAI traffic on private paths. This is engineering around AWS cloud-native capability, in a regulated setting. You set the reference architecture that defines the AWS-native way to build on this platform, run the migration waves that move existing services onto it, hold the standard that new capability is built cloud-native by default, and deliver GxP-qualified AWS infrastructure so regulated GenAI workloads can run there. You lead a team of AWS platform engineers and the AWS agentic engineering capability, and you work with the Azure platform engineering sub-team to keep foundational primitives consistent across clouds without forcing uniformity where each hyperscaler is genuinely better. You will stay ahead of new capabilities and changes to insure constant modernization and evolution. Our Credo grounds how you lead: an inclusive work environment where each person is considered as an individual, diversity and dignity are respected, and merit is recognized. This position will report into the Senior Manager, TS GenAI platform engineering . Key Responsibilities: · Own the GenAI cloud-native modernization roadmap on AWS: assess each service against its managed equivalent, decide replace, refactor, retain or retire, and re-platform onto Bedrock, AgentCore, ECS and EKS, Lambda, Step Functions, DynamoDB and S3. · Define the AWS Well-Architected reference architecture and enforce a cloud-native-by-default standard, favoring managed orchestration — harness engineering and durable workflow on Step Functions — over bespoke agent development. · Engineer the AWS foundation: Bedrock access and quotas; the landing zone and multi-account architecture on Organizations and Control Tower; VPC, PrivateLink, Transit Gateway and least-privilege IAM — delivered for standardized consumption with the Cloud and ISRM teams. · Codify the estate as Infrastructure as Code in Terraform and CDK — modules, pipelines, drift detection, policy-as-code — replacing console changes, contributing hands-on where needed. · Run migration waves without disrupting users: Route 53 weighted and blue-green cutover, backward-compatible APIs, data migration, documented rollback, and decommissioning only once the AWS replacement is proven in production. · Deliver GxP-qualified AWS infrastructure under change control agreed with Quality and ISRM, and partner with GenAI governance on guardrail, identity and traceability controls that reduce dependency on manual review. · Engineer the hosting substrate for the enterprise platform capabilities — APIM, Content Sphere, Agent Framework, Drafting Assist, the MCP Gateway and the Agent, Skills and MCP catalog — plus the semantic layer, retrieval and evaluation harness services that agentic and application workloads build on. · Own cost, performance and resilience engineering: tagging and showback, commitment discounts, Bedrock inference efficiency, multi-AZ and multi-region design, capacity planning and disaster recovery — with CloudWatch and X-Ray observability and reporting on adoption, reliability and cost. · Lead, coach and develop the AWS platform engineering team in an inclusive, Credo-grounded environment — accountable for timely delivery, budget optimization and engineering quality, and supporting LFRP planning. · Own the AWS partnership and cross-cloud alignment: architecture reviews, early access and quota negotiation; position papers on emerging capabilities; adoption of agentic approaches in software development; and parity with the Azure platform engineering sub-team on shared primitives. Qualifications: · Minimum of 8 years of progressive experience in cloud infrastructure or platform engineering, including at least 2 years of formal people-leadership experience managing engineers, or demonstrated equivalent leadership of engineering teams. · Bachelor's degree in Computer Science, Engineering, Information Technology or a related technical discipline is required; a Master's degree or a Ph.D. is preferred. · Demonstrated hands-on ownership of a production AWS estate at enterprise scale, including multi-account architecture, landing zone design and the operational reality of running it. · Proven experience delivering infrastructure through Infrastructure as Code as the primary interface, with a track record of eliminating manual console changes in favor of reviewed, versioned code. · Experience engineering cloud infrastructure in a regulated environment — GxP, validated systems, or an equivalent regime such as SOX or a comparable audited control framework — and producing evidence that satisfies auditors. · Practical experience supporting AI, machine learning or Generative AI workloads on AWS, including the networking, identity and capacity considerations specific to managed inference services. · Demonstrated ability to manage cloud cost and performance as engineering problems, with specific examples of