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Lead Engineer, Evidence Management Digital Ecosystem

Scientific Affairs and Regulatory Affairs | Appian-first engineering and application architecture leadership

This is primarily a hybrid role for candidates currently based within commuting distance of a Johnson & Johnson office in the United States (preferably New Jersey).  Remote candidates may also be considered on a limited basis if they hold strong credentials and required experience.

The Lead Engineer, Evidence Management is responsible for engineering leadership, application architecture, and delivery governance for the Evidence Management digital ecosystem spanning Scientific Affairs and Regulatory Affairs.

This is primarily an Appian-first, AI-enabled, and ecosystem-oriented engineering role. The successful candidate must bring strong hands-on and strategic experience with Appian platform architecture, Appian solution design, Appian integrations, application architecture, engineering quality practices, and SAFe delivery. Most work expectations remain primarily focused on the Regulatory Affairs domain, with Scientific Affairs included as an adjacent and expanding evidence management stakeholder group.

Mandatory Capability Requirement: Candidates will be evaluated for demonstrated ability to engineer, architect, and deliver secure, compliant, scalable solutions that create measurable business value for Regulatory Affairs and support evidence management needs across Scientific Affairs. Appian technology skills are mandatory for this position.

• Serve as the Lead Engineer for evidence management capabilities covering regulatory and scientifc workflow, case management, process automation, content/data flows, integrations, and user experience.
• Define and guide application architecture for platforms supporting regulatory planning, submissions, evidence intake, evidence assessment, regulatory intelligence, workflow automation, post-approval activities, scientific evidence collaboration, and analytics.
• Maintain primary focus on Regulatory Affairs delivery priorities while designing solutions that can also support Scientific Affairs processes, evidence workflows, and cross-functional collaboration.
• Lead AI-enabled modernization by identifying high-value evidence management use cases, designing scalable implementation patterns, and ensuring responsible adoption in regulated environments.
• Partner with Regulatory Affairs, Scientific Affairs, Quality, R&D, Supply Chain, Cybersecurity, Privacy, Data Governance, and IT delivery teams to translate complex business needs into compliant, maintainable technology solutions.
• Act as the lead technical advisor for Appian application decisions, integrations, data products, engineering practices, and AI-enabled capabilities across the evidence management ecosystem.

·       Own and maintain the application architecture strategy and engineering roadmap for evidence management capabilities across Regulatory Affairs and Scientific Affairs.

·       Design scalable, interoperable, and compliant solutions across regulatory platforms, Appian applications, data flows, workflows, integrations, content repositories, and analytics capabilities.

·       Translate complex regulatory and scientific evidence processes into practical application designs, engineering patterns, data models, and delivery backlogs.

·       Drive design governance for new capabilities, major enhancements, technical debt remediation, platform modernization, and cross-system interoperability.

·       Ensure solution designs align with enterprise standards for cloud, cybersecurity, privacy, lifecycle management, supportability, and technical sustainability.

·       Lead engineering and application architecture for Appian-based regulatory workflow, evidence case management, process automation, integration, records, reporting, and user experience capabilities and define Appian design standards

·       Maintain deep understanding of Regulatory Affairs operating processes and technology needs across planning, submissions, regulatory intelligence, workflow orchestration, post-approval activities, content management, compliance, and analytics.

·       Ensure evidence management capabilities support traceability, auditability, role-based access, status visibility, decision history, and inspection-ready records where required.

·       Extend the evidence management architecture and engineering patterns to support Scientific Affairs use cases such as evidence intake, evidence evaluation, collaboration workflows, knowledge search, scientific content traceability, and analytics.

·       Identify common process, data, content, workflow, and integration patterns that can be reused across Scientific Affairs and Regulatory Affairs while respecting domain-specific controls.

·       Define the AI-enabled engineering roadmap for evidence management, including Generative AI, Machine Learning, NLP, semantic search, knowledge graphs, agentic workflows, and intelligent automation where appropriate.

·       Guide proof-of-concepts, pilots, and scaled implementations for AI capabilities with clear business value, security, usability, and compliance controls.

·       Establish patterns for monitoring, hallucination risk mitigation, source traceability, human-in-the-loop review, prompt/version governance, and model performance evaluation.

·       Define data architecture principles for evidence metadata, Appian data models, regulatory master data, scientific evidence content, operational data, unstructured content, and analytics-ready data products.

·       Establish engineering standards for APIs, event-driven integration, workflow orchestration, data lineage, data quality, access control, and platform interoperability.

·       Ensure engineering and architecture decisions support GxP, SOX, privacy, cybersecurity, auditability, data integrity, validation or qualification expectations, and regulated SDLC or computerized system assurance practices.

·       Partner with Quality, Security, Privacy, Regulatory Affairs, Scientific Affairs, and Data Governance stakeholders to assess technology, Appian-specific, evidence-management-specific, and AI-specific risks.

• Bachelor degree in Computer Science, Engineering, Information Systems, Data Science, AI/ML, or a related field.
• 10+ years of progressive experience in software engineering, application architecture, solution architecture, platform architecture, low-code platform architecture, data architecture, or enterprise technology delivery.
• Mandatory hands-on experience engineering, architecting, and delivering solutions on the Appian platform.
• Demonstrated expertise in Appian solution design, including process modeling, interfaces, records, integrations, data models, rules, reports, role-based security, application lifecycle governance, and environment management.
• Strong experience leading application engineering delivery in Agile or SAFe environments, including backlog shaping, technical design, code quality, release readiness, dependency management, and production support considerations.
• Demonstrated experience architecting or engineering AI-enabled enterprise solutions, including Generative AI and/or Machine Learning use cases.
• Hands-on knowledge of Large Language Models, prompt engineering, semantic search, vector databases, Retrieval-Augmented Generation, model orchestration, AI observability, and responsible AI controls.
• Experience designing secure, cloud-native, API-enabled, workflow-enabled, and data-centric platforms in complex enterprise environments.
• Experience supporting regulated business functions such as Regulatory Affairs, Scientific Affairs, Quality, Clinical, Safety, R&D, or Life Sciences technology.
• Working knowledge of GxP, SOX, privacy, cybersecurity, data integrity, regulated SDLC, validation, qualification, or computerized system assurance practices.
• Strong ability to translate business operating models, regulatory processes, and evidence management needs into scalable technical architectures and delivery-ready engineering designs.

• Appian certification or equivalent demonstrated enterprise Appian engineering and architecture experience.
• Experience with Regulatory Affairs platforms, RIM solutions, submission management, regulatory intelligence, regulatory analytics, content management, knowledge management systems, or evidence management platforms.
• Experience supporting Scientific Affairs, Medical Affairs, evidence generation, scientific content workflows, or knowledge search capabilities.
• Experience with Microsoft Azure AI services, Azure OpenAI, Copilot extensibility, AI Foundry, agent frameworks, or equivalent enterprise AI platforms.
• Experience applying AI in regulated environments, including human-in-the-loop workflows, validation strategy, model monitoring, data governance, and risk-based controls.
• Architecture, engineering, or Agile certifications such as TOGAF, Azure Solutions Architect, Appian certification, SAFe Architect, SAFe Agilist, or equivalent.
• Experience leading vendor technology assessments, Appian solution reviews, AI platform evaluations, proof-of-concepts, and enterprise-scale implementation planning.

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