Director R&D AI Systems

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: People Leader All Job Posting Locations: Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America Job Description: We are searching for the best talent for Director, R&D AI Systems to be located in Titusville, NJ, Spring House, PA or Raritan, NJ. The Director, R&D AI Systems is responsible for leading the technology capabilities that operationalize AI, GenAI, LLM, agentic, knowledge graph, and model lifecycle platforms across Innovative Medicine R&D. The role ensures that AI products move from experimentation to reliable, secure, governed, scalable, observable, and cost-effective production services. This leader partners across DDSAI (R&D DATA SCIENCE TEAM), Technology Services, Information Security & Risk Management, Enterprise Architecture, data product teams, model builders, product owners, and business stakeholders to run an integrated Data & AI operating model. The role translates AI use cases, model evaluation needs, and business priorities into production-grade platforms, engineering practices, deployment patterns, and operational controls. The role is accountable for MLOps and LLMOps management, model and agent deployment, agentic platform operations, knowledge graph enablement, AI engineering best practices, AI scorecards, token cost management, security red-teaming, third-party model licensing and SLAs, enterprise GenAI governance, and approved agentic development patterns. Key Responsibilities MLOps and LLMOps Platform Management Lead strategy, operations, and adoption for Cross R&D MLOps and LLMOps platforms, and approved enterprise model lifecycle tooling. Establish repeatable workflows for model registration, packaging, testing, deployment, monitoring, rollback, lifecycle management, and model retirement. Partner with DDSAI model builders and researchers to harden models for regulated, scalable, production-grade deployment. Ensure MLOps and LLMOps platforms meet security, privacy, compliance, resilience, auditability, and operational requirements. Define platform health metrics, adoption targets, service levels, cost controls, and operational governance for model lifecycle platforms. Model Integration, Deployment and Scaling Own model integration patterns, runtime services, APIs, deployment pipelines, scaling approaches, observability, and production support for AI-enabled products. Drive standard approaches for integrating proprietary, open-source, vendor-hosted, and third-party models into R&D applications and workflows. Establish deployment patterns that support batch, real-time, streaming, user-in-the-loop, and agent-assisted use cases. Partner with product, engineering, architecture, infrastructure, and cybersecurity teams to ensure model services are available, performant, resilient, and supportable. Scale model services across functions while managing versioning, dependency management, release readiness, and production change control. Agentic Platform Management and Approved Agent Patterns Lead management of agentic platforms, including orchestration, tool integration, memory/context services, evaluation harnesses, deployment, scalability, observability, and runtime operations. Drive agentic development on approved enterprise patterns, ensuring alignment to architecture, security, privacy, validation, observability, and supportability expectations. Partner with DDSAI and product teams on agent research, experimentation, orchestration, harnessing, and transition to production-grade implementation. Create reusable components, templates, and engineering accelerators for agentic workflows, tool calling, retrieval, human review, and escalation paths. Ensure agentic solutions can be monitored for quality, latency, tool performance, safety, drift, user adoption, and business value. Knowledge Graph, Semantics and Context Engineering Enable knowledge graph capabilities, ontology integration, semantic layers, entity resolution, vector services, embedding pipelines, and reusable context assets for R&D AI products. Partner with DDSAI semantic, ontology, data product, and model teams to translate knowledge representation needs into scalable technology platforms and services. Support governed retrieval patterns that connect knowledge graphs, vector databases, metadata, lineage, and business rules to AI and agentic experiences. Establish operational practices for knowledge graph updates, quality, lineage, access controls, metadata capture, and interoperability with enterprise data platforms. Promote reuse of semantic assets, canonical entities, knowledge services, and context engineering patterns across Cross R&D AI products. AI Engineering Best Practices and Adoption Define and embed AI engineering best practices across Cross R&D, including prompt engineering, retrieval patterns, model integration, evaluation, test automation, CI/CD, DevSecOps, monitoring, and release management. Create engineering standards, reusable patterns, playbooks, training, communities of practice, and enablement programs that accelerate safe adoption of AI capabilities. Partner with product and platform teams to improve developer productivity, reduce duplicate patterns, and strengthen consistency across AI product delivery. Implement approved design patterns for responsible AI, human oversight, auditability, explainability, data protection, and regulated environment readiness. Drive adoption metrics for AI engineering practices, including evidence of reuse, quality improvement, cycle-time reduction, and reduced production defects. AI Scorecards, Observability and Value Management Establish AI scorecards that measure quality, accuracy, robustness, latency, scalability, safety, reliability, adoption, user satisfaction, and business impact