Principal Enterprise Architect - AI & Agents (Remote)

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.