Senior Manager, SAP Business Technology Platform AI Engineering
Johnson & Johnson
September 02, 2026
On-site
Raritan, NJ
IT
Senior Manager, SAP Business Technology Platform (BTP) AI Engineering — Raritan, NJ
Key Responsibilities
- Lead SAP BTP AI Engineering supporting S/4HANA transformation across MedTech, Innovative Medicine, and Enterprise Finance.
- Drive AI architecture, engineering standards, reusable services, and delivery frameworks for enterprise AI on SAP BTP.
- Identify, prioritize, develop, and scale high-value AI use cases with S/4 transformation teams.
- Advance ERP AI Software Development Lifecycle (AI SDLC) from PoCs to secure, scalable, production solutions.
- Own AI SDLC roadmap; develop Generative AI and AI-assisted engineering across requirements, architecture, code generation/modernization, testing, documentation, security analysis, deployment, and support.
- Establish reusable AI engineering patterns across SAP technologies (ABAP, RAP, CAP, Fiori/UI5, BTP, APIs, integrations, S/4 extensions) and operationalize agentic engineering.
- Drive SAP Joule/Joule for Developers/Joule Agents adoption; improve developer productivity, quality, speed-to-market, and automation.
- Build GenAI/agentic capabilities (LLM integration, RAG, knowledge, orchestration, tool calling, prompts, evaluation, guardrails, observability) and secure AI-agent interaction with SAP processes/data.
- Align AI roadmap to Technology Services value realization; define KPIs and measure outcomes (productivity, cycle reduction, quality, reduced technical debt, lower operating costs).
- Establish strategic partnerships with SAP and AI vendors; lead pilots/co-development; convert innovations into governed reusable capabilities.
- Ensure responsible AI: security, privacy, regulatory compliance, data governance; controls for authentication/authorization, oversight, auditability, evaluation, monitoring, and lifecycle management.
- Lead and mentor cross-functional engineering teams; communicate strategy/progress/risks/value to senior leadership.
Required Qualifications
- 12+ years technology experience; significant SAP engineering/enterprise platforms/large-scale transformation.
- Bachelor’s or Master’s in CS/IT/Software Engineering/Data Science or related field.
- Strong experience with SAP BTP, SAP S/4HANA, and enterprise application development; knowledge of Generative AI/Agentic AI/SAP Business AI/Joule and AI-assisted software engineering.
- Desirable: SAP BTP, SAP S/4HANA, AI/ML certifications.
- Strong SAP development knowledge (ABAP, RAP, CAP, Fiori/UI5, APIs, integrations, BTP extension architectures).
- Experience transforming emerging tech from experimentation/POC to enterprise production.
- Proven leadership managing large-scale SAP transformations and influencing senior stakeholders.
- Strong enterprise architecture, security, responsible AI, and technology governance knowledge.
Preferred Qualifications
- Experience with Joule for Developers, SAP AI Foundation, RAG, vector databases, knowledge graphs, AI agents/multi-agent, MCP/tool integration, AI evaluation, observability, and guardrails.
- Experience building enterprise AI engineering platforms, reusable AI services/accelerators, and measurable value-realization frameworks.
Key Responsibilities
- Lead SAP BTP AI Engineering supporting S/4HANA transformation across MedTech, Innovative Medicine, and Enterprise Finance.
- Drive AI architecture, engineering standards, reusable services, and delivery frameworks for enterprise AI on SAP BTP.
- Identify, prioritize, develop, and scale high-value AI use cases with S/4 transformation teams.
- Advance ERP AI Software Development Lifecycle (AI SDLC) from PoCs to secure, scalable, production solutions.
- Own AI SDLC roadmap; develop Generative AI and AI-assisted engineering across requirements, architecture, code generation/modernization, testing, documentation, security analysis, deployment, and support.
- Establish reusable AI engineering patterns across SAP technologies (ABAP, RAP, CAP, Fiori/UI5, BTP, APIs, integrations, S/4 extensions) and operationalize agentic engineering.
- Drive SAP Joule/Joule for Developers/Joule Agents adoption; improve developer productivity, quality, speed-to-market, and automation.
- Build GenAI/agentic capabilities (LLM integration, RAG, knowledge, orchestration, tool calling, prompts, evaluation, guardrails, observability) and secure AI-agent interaction with SAP processes/data.
- Align AI roadmap to Technology Services value realization; define KPIs and measure outcomes (productivity, cycle reduction, quality, reduced technical debt, lower operating costs).
- Establish strategic partnerships with SAP and AI vendors; lead pilots/co-development; convert innovations into governed reusable capabilities.
- Ensure responsible AI: security, privacy, regulatory compliance, data governance; controls for authentication/authorization, oversight, auditability, evaluation, monitoring, and lifecycle management.
- Lead and mentor cross-functional engineering teams; communicate strategy/progress/risks/value to senior leadership.
Required Qualifications
- 12+ years technology experience; significant SAP engineering/enterprise platforms/large-scale transformation.
- Bachelor’s or Master’s in CS/IT/Software Engineering/Data Science or related field.
- Strong experience with SAP BTP, SAP S/4HANA, and enterprise application development; knowledge of Generative AI/Agentic AI/SAP Business AI/Joule and AI-assisted software engineering.
- Desirable: SAP BTP, SAP S/4HANA, AI/ML certifications.
- Strong SAP development knowledge (ABAP, RAP, CAP, Fiori/UI5, APIs, integrations, BTP extension architectures).
- Experience transforming emerging tech from experimentation/POC to enterprise production.
- Proven leadership managing large-scale SAP transformations and influencing senior stakeholders.
- Strong enterprise architecture, security, responsible AI, and technology governance knowledge.
Preferred Qualifications
- Experience with Joule for Developers, SAP AI Foundation, RAG, vector databases, knowledge graphs, AI agents/multi-agent, MCP/tool integration, AI evaluation, observability, and guardrails.
- Experience building enterprise AI engineering platforms, reusable AI services/accelerators, and measurable value-realization frameworks.