Architecture Design & Standards
• Design scalable, secure and cost-efficient AI system architectures for regional deployments.
• Define and enforce technical standards, design patterns and architectural guardrails for the team.
• Lead build vs buy vs integrate decisions, evaluating platforms and vendors.
• Produce architecture artefacts (solution designs, data flows, integration maps, technical roadmaps).
• Architect solutions across relevant cloud platforms (Azure, AWS and compliant options as required).
• Design for multi-cloud and hybrid environments where regulations demand.
• Ensure infrastructure meets data sovereignty requirements across APAC countries.
• Review cloud cost architecture to balance performance and spend.
• Ensure AI development and deployment environments are designed for reliability, reproducibility, access control and operational support.
AI Platform & Model Strategy
• Designs and governs AI and machine learning environments, including patterns for experimentation, deployment, monitoring and operational support.
• Evaluate and select foundational models, fine-tuning approaches and RAG architectures appropriate to each use case.
• Design MLOps and LLMOps pipelines (model versioning, evaluation, monitoring, retraining triggers).
• Define the regional AI platform strategy (shared services, reusable components, integration patterns).
• Stay current on model developments and assess architectural implications for the roadmap.
• Embed security controls (encryption, access controls, audit logging) into all architectural decisions.
• Align architecture to global governance standards and partner with security and risk stakeholders.
• Conduct technical risk assessments for new AI initiatives and define mitigation architecture.