AI Enterprise Architect

About the role

The AI Enterprise Architect joins FAB’s AI Hub Innovation team, responsible for designing, governing, and evolving the AI architecture of solutions. This role ensures alignment with business strategy, technology standards, and regulatory requirements.

Key responsibilities

• Produce Architecture Artifacts such as Architecture Decision Records (ADR) and Solution Architecture Design (SAD).
• Define and maintain the enterprise AI architecture, ensuring alignment with business goals, technology standards, and regulatory requirements.
• Lead evaluation, selection, and adoption of AI technologies, platforms, and tools across initiatives.
• Develop and enforce architectural standards, best practices, and governance frameworks for AI initiatives.
• Collaborate with business, data, and technology teams to design scalable, secure, and cost‑effective end‑to‑end AI solutions.
• Oversee integration of AI/ML models into production systems, ensuring reliability, performance, and maintainability.
• Guide architects, engineers, and scientists on application strategy, model lifecycle management, and MLOps practices.

Required profile

• 5+ years of experience in enterprise architecture with a strong focus on AI, data, or advanced analytics in large‑scale environments.
• Proven track record designing and implementing AI/ML platforms, frameworks, and reference architectures at enterprise scale.
• Deep understanding of the AI/ML lifecycle, including data engineering, model development, deployment, monitoring, and governance.
• Expertise in cloud‑native architectures, hybrid/multi‑cloud strategies, and integration of AI solutions with existing enterprise systems.
• Strong knowledge of data privacy, security, and regulatory compliance (e.g., GDPR, local data laws) as they relate to AI.
• Ability to translate business strategy into AI architecture roadmaps and actionable blueprints.
• Excellent communication and stakeholder management skills, influencing both technical and non‑technical audiences.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field; relevant certifications (e.g., TOGAF, AWS/Azure Architect, AI/ML certifications) preferred.
• Experience in banking or other highly regulated industries is a plus.

Required skills

• AI/ML platforms
• Cloud‑native architecture
• Hybrid/multi‑cloud environments
• MLOps practices
• AWS
• Azure
• TOGAF