Principal Enterprise Architect - AI & Agents

Principal Enterprise Architect – Ai & Agentic Solutions

Within IQVIA's AI & Technology Solutions (ATS) organization, the Architecture & Standards (A&S) group defines "the IQVIA way" of building technology. The Enterprise Architecture (EA) function, operating within A&S, is responsible for translating business strategy and OKRs into scalable, standards-driven architectures that reduce technical debt, enable reuse, and transform business processes.

The Enterprise Architecture organization is seeking a Principal Enterprise Architect – AI & Agentic Solutions to define, govern, and evolve enterprise architecture strategy, standards, and roadmaps across a large, global technology landscape. This role plays a critical leadership position in shaping how AI and agentic capabilities are designed, governed, and adopted across IQVIA.

The ideal candidate combines strategic vision with architectural rigor. They understand how emerging AI technologies can reshape research and data operations within a CRO environment while ensuring alignment with enterprise principles, governance, and compliance requirements. This leader balances innovation with pragmatism and can translate complex AI concepts into structured capabilities, reference architectures, and reusable patterns. The role requires comfort engaging across engineering, data science, and senior business stakeholders to deliver responsible, scalable, and standards-driven enterprise AI solutions.

Job Overview

The Principal Enterprise Architect is accountable for co-designing target-state architectures and reference models for AI and agentic solutions, establishing enterprise guardrails (patterns, standards, and decision frameworks), and providing architectural oversight for priority initiatives.

This role maintains a portfolio-wide perspective of business and technical capabilities, identifying cross-cutting needs and opportunities to consolidate platforms and services across application and data domains. The Architect champions interoperability and reuse through modern architectural approaches, cloud-native delivery, and modern data platforms deployed on Azure and AWS.

What success looks like:

· Published and broadly adopted AI and agent reference architectures

• Increased reuse of shared services, patterns, and templates
• Reduced duplication of AI implementations and development efforts
• Faster architecture reviews and approvals with fewer exceptions
• Improved compliance readiness for regulated and high-risk AI workloads

Key Responsibilities

· Define, maintain, and evolve enterprise reference architectures, standards, and reusable patterns for generative AI and agentic systems.

• Govern solution designs, including selection of appropriate technical components, ensuring delivery teams can adopt standards with minimal friction.
• Provide architectural oversight for major AI initiatives, guiding trade-offs across security, compliance, scalability, cost, and time-to-value.
• Translate business strategies and OKRs into capability-based roadmaps and target-state architectures, avoiding project-specific point solutions.
• Drive enterprise interoperability across systems and data domains by defining integration patterns and data exchange approaches aligned with enterprise standards.
• Partner with compliance, risk, and information security teams to ensure architectures align with global regulatory and quality expectations (e.g., GxP, GDPR, HIPAA, EU AI Act).
• Maintain enterprise capability models and architecture artifacts using EA tools (e.g., LeanIX, Ardoq, MEGA), mapping capabilities to processes, systems, products, outcomes, and key data domains.
• Identify opportunities to consolidate platforms and services across business units, quantifying investment implications and business benefits.
• Guide senior stakeholders through architectural options, trade-offs, and investment decisions using clear, business-focused narratives and models.
• Monitor AI, agentic, and life sciences technology trends; assess relevance and fit; and inform enterprise roadmaps, reference architectures, and innovation priorities.
• Mentor and influence architects and senior engineers to raise overall architecture maturity and adherence to enterprise standards.

Required Qualifications

· 10+ years of experience in enterprise or application architecture within large, complex organizations, including ownership of standards, roadmaps, and governance.

• Proven experience designing and scaling generative AI and agentic solutions, with a strong focus on reusable patterns and operationalization.
• Deep technical knowledge of generative AI and agentic concepts, including vector databases, RAG, tool-use, MCP servers, HITL, evaluation frameworks, monitoring, observability, and platforms such as LangGraph.
• Strong cloud architecture experience on Azure and/or AWS, including modern data architectures (lakehouse, governance, cataloguing).
• Expertise in modern integration styles (API-first, event-driven, microservices) and the ability to codify them into enterprise standards and reference architectures.
• Exceptional communication, influence, and stakeholder management skills, including engagement with senior business, product, and technology leaders.
• Experience building business cases for modernization, rationalization, and platform consolidation initiatives.

Preferred Qualifications

· Experience in healthcare or life sciences, with strong knowledge of the clinical trial lifecycle, real-world evidence, or commercialization of clinical assets.

• Hands-on partnership with CRO business teams, including study design, site activation and management, clinical operations, regulatory affairs, medical affairs and KOL engagement, real-world safety and efficacy, pharmacovigilance, and EMR ecosystems.
• Proficiency with enterprise architecture tools (LeanIX, Ardoq, MEGA, or similar) and formal capability modeling methodologies.