The Digital Enablement Lead supports the Digital transformation within the Technology Quality (TQ) team. This role drives AI-enabled process improvements, intelligent automation, and data driven decision support across Computer Systems Validation (CSV) processes. The Digital Enablement Lead identifies and validates high-impact use cases, partners with stakeholders to translate business problems into solutions, build prototypes, and lead adoption through effective partnership, training, and governance. This position combines AI/ML software development lifecycle expertise and CSV experience with a strong ability to translate strategy into scalable solutions. Designs and implements practical AI-driven user cases partnering with stakeholders to translate business problems into measurable, implementable AI use cases. Runs feasibility and impact analyses, build prototypes, and drive adoption through effective stakeholder enablement. Ensures the quality of AI-generated outputs, agent behaviors, and AI-assisted workflows by building benchmark scenarios, defining scoring rubrics, evaluating business usefulness, and identifying failure patterns. Owns end‑to‑end governance, validation, and lifecycle oversight of AI agents used across CSV processes, ensuring they remain fit for intended use, compliant, controlled, and auditable throughout deployment and operation. Partners with Technology Quality leaders and SMEs to define the AI roadmap, maintaining a prioritized use-case catalog, and aligning initiatives to business goals. Translates business needs into structured use cases with objectives, success metrics, data requirements, risks, controls, and proposed models/techniques. Maintains an AI/Agent Inventory including control over Intended Use. Creates lightweight prototypes (scripts, dashboards, prompt experiments) to validate solution hypotheses and value case. Design and implement AI solutions for CSV processes enabling decision support, and workflow automation. Collaborate with data Science, engineering, and other technology teams to define data pipelines, model evaluation criteria, acceptance tests, and integration requirements. Translate business processes into product requirements, user stories, and acceptance criteria for data scientists, ML engineers, and product teams. Compare performance across prompt versions, workflow revisions, tools, and models; surface evidence for release decisions and regression/improvement. Establish benchmark scenarios and scoring rubrics to evaluate AI outputs, agent behaviors, and workflows in real business contexts. Assess outputs for groundedness, instruction adherence, consistency, usefulness, tone, control compliance, and risk; identify hallucinations, unsupported assertions, missing logic, and unsafe recommendations. Prepare test scripts, coordinate UAT, document outcomes, and support validation and release documentation. Define prompt design strategies, guardrails, and explainability requirements; implement human-in-the-loop controls and risk mitigation strategies. Ensure all AI use cases align with J&J Responsible AI ,GenAI and Q&C usage guidelines Partner with SMEs to monitor ethical, privacy, and regulatory considerations for each use case (e.g., 21 CFR Part 11, GxP, Annex).
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Job Type
Full-time
Career Level
Mid Level