About the Role:
This role sits within the AI Hub Innovation team, where the AI Enterprise Architect is responsible for designing, governing, and evolving the AI architecture of the solutions. The position ensures alignment with business strategy, goals, technology standards, and regulatory requirements.
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Key Responsibilities:
- Responsible for producing 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 the 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 end-to-end AI solutions that are scalable, secure, and cost-effective.
- 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.
Requirements / Qualifications:
- 5+ years of experience in enterprise architecture, with a strong focus on AI, data, or advanced analytics in large-scale environments.
- Proven track record in designing and implementing AI/ML platforms, frameworks, and reference architectures at enterprise scale.
- Deep understanding of 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, with the ability to influence both technical and non-technical audiences.
- Educational qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field; relevant certifications (e.g., TOGAF, AWS/Azure Architect, AI/ML certifications) are preferred.
Preferred / Beneficial:
- Experience in banking or other highly regulated industries preferred.
