Head of AI Agentic Architecture

SSCOR · FR, Paris

The Head of Agentic Architecture defines and governs 'what good looks like' for agentic systems across the enterprise. This role is accountable for architecture principles, standards, and assurance for agentic systems. He/she shapes the enterprise landscape by optimally integrating agentic systems across business, data, application, and technology architectures. The role establishes the standards for how teams design, build, test, operate, and scale agentic systems, ensuring they are safe, effective, measurable, and aligned with the cloud platform and SDLC.

Key duties and responsibilities

• We are seeking a highly experienced and forward-looking leader to define, govern, and continuously evolve the architecture of agentic systems across our global reinsurance business.
• This role will establish agentic reference architecture, design principles, and governance frameworks, define architecture review criteria and quality standards, and ensure alignment with enterprise architecture, data strategy, and regulatory expectations.
• This role does not own delivery or squads.
• In close collaboration with the AI unit in Tech, Data & AI department, this role will guide the organization on designing, building, and governing intelligent agents that enhance underwriting, claim processing, risk modeling, and operational efficiency. The position will play a key role in shaping how autonomous and semi-autonomous systems are safely and effectively deployed in a highly regulated, risk-sensitive environment.
• Monitor, test, and promote agentic engineering best practices across delivery teams and platforms.
• Define “what good looks like” for agentic development, including standards, patterns, quality criteria, operating models, and success measures.
• Work closely with the cloud engineering team responsible for the cloud platform and SDLC implementation to ensure agentic engineering is embedded into platform capabilities, tooling, governance, and delivery workflows.
• Act as the champion and visible advocate for agentic development across the organization.

Key Responsibilities:

• Define What Good Looks Like for Agentic Architecture
  • Establish enterprise-wide architecture principles for agentic systems
  • Define standards including explainability, auditability, traceability, and human oversight
  • Develop reference architectures aligned with enterprise architecture
• Architecture Governance & Assurance
  • Own architecture review frameworks
  • Define guardrails and ensure compliance with risk, regulatory, and data standards
  • Provide architecture assurance across agentic initiatives
• Shape the Agentic Portfolio (Non-Delivery)
  • Guide structuring of agentic use cases aligned with strategy and architecture
  • Act as strategic advisor, not delivery owner
• Partner with Engineering, AI, and Platform Teams: Collaborate with Product & Engineering, AI Tech & Ops, and Enterprise Architecture
• Embed Architecture in Cloud Platform & SDLC and AI pipelines
• Influence platform for orchestration, observability, and secure data access
• Thought Leadership: Act as thought leader and promote responsible agentic adoption.

Success Measures:

• Teams have clear, practical standards for building and operating agentic solutions.
• Agentic engineering practices are embedded into the cloud platform and SDLC rather than treated as separate activities.
• Delivery teams demonstrate improved capability, consistency, quality, and confidence in agentic development.
• Agentic solutions are evaluated, monitored, and governed with repeatable methods.

The organization has an active community of practitioners and a visible champion for agentic engineering.

Required experience & competencies

• Strong architecture leadership experience.

• Deep understanding of cloud platforms, DevSecOps, SDLC governance, automation, observability, and production operations.

• Practical experience with AI-assisted and agentic development approaches, including evaluation, testing, guardrails, and operational risk management.

• Ability to translate emerging technology into pragmatic standards, patterns, and enablement materials.

• Excellent stakeholder management, facilitation, coaching, and communication skills.

Required Education 

• Master’s degree in Computer Science