Knowledge Graph Engineer Jobs in Philadelphia, PA

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, andsolutions 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 ResponsibilitiesMLOps 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.
• Define model and agent observability standards, dashboards, alerts, logs, traces, drift monitoring, evaluation results, and operational health indicators.
• Partner with DDSAI on model and agent evaluations, including accuracy, robustness, safety, drift, and fitness for productization.
• Translate AI scorecard insights into improvement plans, roadmap priorities, operational interventions, and executive-level reporting.
• Ensure AI products have clear OKRs, success metrics, support models, adoption targets, and value realization mechanisms.

Tokenomics and AI Cost Management

• Lead tokenomics and AI cost management across Cross R&D AI systems, including token consumption, prompt efficiency, model selection, caching, routing, inference cost, and workload optimization.
• Establish spend transparency, chargeback/showback, forecasting, threshold alerts, and cost-to-value reporting for GenAI and model workloads.
• Partner with product teams to optimize model selection, context windows, retrieval strategies, batching, latency, quality, and cost tradeoffs.
• Drive disciplined management of cloud, GPU, inference, vectorization, knowledge graph, embedding, and third-party model costs.
• Use tokenomics insights to inform product roadmaps, usage policies, vendor decisions, and investment prioritization.

Security Red-Teaming, Model Risk and Resilience

• Lead security red-teaming processes for AI, GenAI, LLM, agentic, and knowledge-enabled systems in collaboration with ISRM, cybersecurity, architecture, and product teams.
• Establish testing patterns for prompt injection, data leakage, unsafe tool use, model abuse, hallucination risks, adversarial inputs, access control gaps, and third-party model risks.
• Ensure AI systems are designed with secure-by-design principles, runtime controls, responsible use guardrails, human oversight, logging, and escalation paths.
• Coordinate remediation of red-team findings, vulnerabilities, audit issues, resilience gaps, and operational risks before and after production release.
• Maintain compliance with enterprise security, privacy, legal, regulatory, and quality expectations for AI-enabled products.

Third-Party Model Licensing, SLAs and Vendor Management

• Manage technology aspects of third-party model licensing, service-level agreements, vendor dependencies, model access patterns, usage terms, and operational commitments.
• Partner with procurement, legal, privacy, Cybersecurityarchitecture, and product leadership to evaluate model providers and platform vendors.
• Ensure third-party model consumption is governed through approved patterns, access controls, data-use constraints, cost controls, monitoring, and performance expectations.
• Track vendor performance, availability, latency, support responsiveness, compliance obligations, and enterprise risk posture.
• Develop contingency plans for model/provider changes, performance degradation, availability issues, licensing changes, and exit strategies.

Enterprise GenAI Governance and Integrated Ways of Working

• Lead implementation of enterprise GenAI governance for Cross R&D AI systems, including intake, prioritization, standards adherence, tech stack alignment, deployment readiness, monitoring, compliance, and lifecycle governance.
• Operationalize integrated DDSAI/JJT ways of working for data and AI initiatives, with clear accountability across product framing, engineering, model evaluation, platform operations, governance, financial planning, and lifecycle management.
• Use systems of record and portfolio governance routines to provide transparent tracking of AI initiatives, milestones, financials, risks, dependencies, and value realization.
• Clarify collaboration points across JJT, DDSAI, Technology Services, Cybersecurity, Enterprise Architecture, external vendors, and business stakeholders.
• Ensure AI systems follow approved enterprise patterns while preserving speed, innovation, user experience, and measurable patient and business impact.

Qualifications

• Master's degree in Computer Science, Information Technology, Data Science, Engineering, or related field; advanced degree preferred.
• 10+ years of progressive technology leadership experience with significant focus on AI/ML platforms, MLOps, LLMOps, data platforms, software engineering, cloud, product delivery.
• Expertise in enterprise cloud platforms (Azure, AWS, or GCP) with hands-on experience deploying and operating AI/ML workloads at scale and managed AI services.
• Experience in life sciences, healthcare, biotech, pharmaceuticals, or other highly regulated environments preferred.
• Hands-on experience with MLOps/LLMOps enterprise model lifecycle platforms.
• Demonstrated expertise in model deployment, APIs, cloud services, observability, DevSecOps, CI/CD, prompt/retrieval patterns, vector databases, knowledge graphs, or agentic platforms.
• Expertise in LLM platform integration and orchestration, including experience with OpenAI API, Anthropic Claude, AWS Bedrock, or Google Vertex AI, and familiarity with frameworks such as LangChain, LlamaIndex, LangFuse, or AutoGen for building production-grade GenAI applications.
• Strong understanding of AI governance, security red-teaming, model risk management, privacy, data protection, responsible AI practices, and third-party model/vendor management.
• Proven ability to lead cross-functional teams and influence DDSAI, technology, security, architecture, product, infrastructure, vendor, and business stakeholders.
• Strong financial and operational discipline, including experience managing platform costs, token consumption, SLAs, service health, adoption, and measurable value realization.

Professional Experience

• Proven success translating AI/GenAI experimentation into reliable, secure, scalable, and observable production capabilities.
• Experience establishing enterprise-ready engineering practices for AI systems, including development standards, deployment patterns, testing, monitoring, documentation, and operating models.
• Demonstrated ability to partner with data science organizations on model evaluation, research handoff, productization, deployment, and lifecycle management.
• Experience managing critical platforms or services with defined reliability targets, operational metrics, incident response, cost optimization, and service improvement routines.
• Strong track record driving adoption of shared platforms, reusable patterns, governance processes, and AI engineering practices across product teams.
• Skilled in managing third-party platforms, model providers, licensing considerations, vendor SLAs, and enterprise risk tradeoffs.
• Strong executive communication skills with the ability to translate AI platform complexity into clear decisions, risk tradeoffs, and business value.

#JNJTech

#LI-Hybrid

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.

Johnson & Johnson is committed to providing an interview process that is inclusive of our app...