Lead Engineer, Evidence Management

Johnson & Johnson Innovative Medicine

4d•$102,000 - $175,950•Hybrid

About The Position

The Lead Engineer, Evidence Management provides engineering leadership, application architecture, and delivery governance for a digital evidence management ecosystem spanning Regulatory Affairs and Scientific Affairs. The role is primarily Appian-first, AI-enabled, and ecosystem-oriented, with most delivery priorities focused on Regulatory Affairs and Scientific Affairs as an adjacent and expanding stakeholder group. The position may be located in Raritan, New Jersey, or Limerick, Ireland. The posting describes the role as primarily hybrid for candidates within commuting distance of a Johnson & Johnson office in the United States, preferably New Jersey; remote candidates may be considered on a limited basis if they have strong credentials and required experience. The Lead Engineer guides architecture and delivery for regulatory and scientific workflows, case management, process automation, content and data flows, integrations, and user experiences. The role shapes solutions supporting regulatory planning, submissions, evidence intake and assessment, regulatory intelligence, post-approval activities, scientific evidence collaboration, and analytics. It also leads AI-enabled modernization by identifying valuable use cases and establishing scalable implementation patterns and responsible adoption controls for regulated environments. The position acts as the lead technical advisor for Appian application decisions, integrations, data products, engineering practices, and AI-enabled capabilities. It partners with Regulatory Affairs, Scientific Affairs, Quality, R&D, Supply Chain, Cybersecurity, Privacy, Data Governance, and IT delivery teams to turn complex business needs into compliant, maintainable technology solutions.

Requirements

Bachelor’s 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.
Hands-on experience engineering, architecting, and delivering solutions on the Appian platform.
Expertise in Appian solution design, including process modeling, interfaces, records, integrations, data models, rules, reports, role-based security, application lifecycle governance, and environment management.
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.
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.
Ability to translate business operating models, regulatory processes, and evidence management needs into scalable technical architectures and delivery-ready engineering designs.

Nice To Haves

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.

Responsibilities

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 regulatory and scientific evidence processes into application designs, engineering patterns, data models, and delivery backlogs.
Govern designs for new capabilities, major enhancements, technical debt remediation, platform modernization, and cross-system interoperability.
Align solution designs with enterprise standards for cloud, cybersecurity, privacy, lifecycle management, supportability, and technical sustainability.
Lead engineering and application architecture for Appian-based regulatory workflows, evidence case management, process automation, integration, records, reporting, and user experience; define Appian design standards.
Maintain a deep understanding of Regulatory Affairs processes and technology needs, including 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 evidence management architecture and engineering patterns to Scientific Affairs use cases, including evidence intake and evaluation, collaboration workflows, knowledge search, scientific content traceability, and analytics.
Identify reusable process, data, content, workflow, and integration patterns 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 AI proof-of-concepts, pilots, and scaled implementations with clear business value, security, usability, and compliance controls.
Establish AI patterns for monitoring, hallucination risk mitigation, source traceability, human-in-the-loop review, prompt and 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.

Benefits

Medical insurance
Dental insurance
Vision insurance
Life insurance
Short- and long-term disability insurance
Business accident insurance
Group legal insurance
Consolidated retirement plan (pension)
Savings plan (401(k))
Vacation: 120 hours per calendar year
Sick time: 40 hours per calendar year (56 hours for Washington State employees)
Holiday pay, including floating holidays: 13 days per calendar year
Work, Personal and Family Time: up to 40 hours per calendar year
Parental Leave: 480 hours within one year of a child’s birth, adoption, or foster care placement
Condolence Leave: 30 days for an immediate family member and 5 days for an extended family member
Caregiver Leave: 10 days
Volunteer Leave: 4 days
Military Spouse Time-Off: 80 hours