Assoc. Dir, Agentic Standards & Context Intelligence

Job Description Generative and agentic AI are reshaping how commercial teams work — from how insights are surfaced, to how content is created and reviewed, to how decisions are made in market. To move at the speed of that opportunity without compromising on trust, compliance, or quality, the Human Health Division is scaling a GenAI & Agentic Center of Excellence: the team that defines the standards, reusable frameworks, and shared context foundations used to build, evaluate, and ship enterprise-grade AI agents across the commercial portfolio. As adoption expands across therapeutic areas, business units, and international markets, two things determine whether agents scale well: an opinionated, well-governed rulebook that every agent team can start from, and a shared context foundation so that agents reason with consistent business meaning rather than re-interpreting raw data differently each time. Standards without context produce compliant but analytically wrong agents; context without standards produces inconsistency at scale. The Associate Director, Agentic Standards & Context Intelligence Lead will lead the Agentic Standards & Context Intelligence pillar within the CoE, owning both halves of that equation. This leader will be accountable for the enterprise standards, playbooks, and specifications that shape how commercial AI agents are designed, deployed, and operated — and for the shared, governed context layer that encodes commercial entities, metric definitions, and domain semantics so agents reason with a common understanding of the business. This individual will partner closely with responsible-AI, engineering, platform, data, business activation, and enterprise governance teams to ensure standards and context are not only published, but activated in the day-to-day work of the teams building AI agents. Primary Responsibilities include: Standards vision and roadmap: Defining and evolving playbooks, specifications, and technical standards across the full agent lifecycle (design, deploy, monitor, improve), and driving them from research-backed drafts through use-case-validated versions. Context intelligence strategy: Owning the strategy for the shared, governed context layer — ontology, knowledge graph, semantic definitions, metric registries, and business glossary — so agents reason with consistent commercial meaning (brands, channels, vendors, KPIs) rather than raw table lookups. Context as infrastructure: Elevating context handling to a first-class, measurable component of the platform, with instrumentation, lifecycle controls, versioning, and health metrics (accuracy, freshness, compliance) that make context quality continuously measurable and improvable at scale. Standards adoption and enablement: Driving socialization and adoption of agentic standards and context patterns across business, CoE, and platform teams through enablement content, starter kits, reference patterns, and developer-facing integrations. Enterprise alignment: Aligning CoE standards and context approaches with broader enterprise architecture, data, and governance functions — clarifying CoE vs. enterprise responsibilities and influencing enterprise-level standards. Cross-pillar partnership: Partnering with responsible-AI, governance, engagement, and platform teams to translate standards and context capabilities into reusable delivery patterns and operational practices. Measurement and continuous improvement: Establishing metrics for standards adoption, context quality, and business impact, and running feedback loops that translate developer and business input into standards and context revisions. Team leadership: Leading, coaching, and scaling a team of technical specialists across standards authorship, context engineering, and adoption. Basic Qualifications Bachelor's degree in Computer Science, Data Science, Engineering, Information Technology, or a related STEM field Experience defining and driving adoption of technical standards, playbooks, or frameworks in a large, matrixed organization Hands-on understanding of the GenAI agent lifecycle (design, deploy, monitor, improve), including retrieval-augmented generation, tool use, orchestration, evaluation, and guardrails Experience with context engineering concepts applied to AI systems — grounding, retrieval strategy, semantic definitions, or shared context layers Experience influencing senior stakeholders across engineering, data, product, and enterprise governance functions Preferred Qualifications Experience in a regulated industry (pharmaceutical, healthcare, financial services) with audit and compliance obligations Experience designing or governing ontology, knowledge graph, semantic layer, business glossary, or metric registry capabilities for analytics or AI consumption Experience instrumenting context or data quality metrics (accuracy, freshness, compliance, lineage) as measurable platform health signals Experience with evaluation-driven and specification-driven development practices for AI agents Experience standing up or scaling an AI center of excellence, platform governance function, or standards program with measurable adoption outcomes Experience leading and mentoring a technical team spanning standards and data/context disciplines Required Skills Agentic AI Standards, AI Governance, AI Playbook Development, Change Management, Context Engineering, Cross-Functional Collaboration, Generative AI (GenAI), Governance Frameworks, Knowledge Graphs & Ontology, Large Language Models (LLMs), Multi-Agent Systems, People Leadership, Program Management, Responsible AI, Retrieval-Augmented Generation (RAG), Semantic Layer Design, Stakeholder Relationship Management, Standards Adoption, Strategic Communications, Technical Leadership Preferred Skills Access-Aware Retrieval, Agent Evaluation, Agent Observability, Business Glossary & Metric Registry Design, Context Health Metrics (Accuracy, Freshness, Compliance), Data Governance for AI, Enterprise Architecture Influence, MLOps / LLMOps, Prompt Engineering Libraries, Regulated-Industry AI Experience