• Define and own end-to-end AI architecture for multiple enterprise use cases, including data flows, model lifecycle, and serving patterns across Azure and AWS. • Design AI/ML solution blueprints covering data ingestion, feature stores, training pipelines, model registry, deployment, monitoring, retraining, and decommissioning. • Establish and standardize MLOps frameworks (MLflow/Kubeflow/Airflow, Docker, Kubernetes) and reference implementations that AI/ML engineers and data scientists can reuse across teams. • Collaborate with product, data, and business stakeholders to identify AI opportunities, shape solution options, and align AI architectures with business and non-functional requirements (scalability, reliability, cost, latency). • Define and enforce governance for model approval, explainability, versioning, drift monitoring, and compliance with data privacy and regulatory requirements relevant to the organization. • Guide the selection and integration of cloud-native services (Azure ML, AWS SageMaker, Lambda, EC2, S3, Azure Functions, API gateways, monitoring stacks) into cohesive AI platforms. • Work closely with security, compliance, data, and enterprise architecture teams to ensure AI solutions meet standards for security, resilience, observability, and cost optimisation. • Provide technical leadership and mentoring to AI engineers, ML engineers, and data scientists on best practices in AI architecture, MLOps, and cloud-native design. • Create and maintain architectural artefacts: high-level designs, detailed solution diagrams, standards, and documentation for AI platforms and reference solutions.