Director R&D AI Systems

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld 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 Med Tech, 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.

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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.

• 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.

• 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.

• 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…