Executive Director, AI Tech Delivery Lead

Key Responsibilities

1. Build-Out Technology Solutioning and Delivery Capability for APAC AI CFT initiatives

• Establish and lead an expert core solutioning & delivery team — defining team structure, roles, career paths, and performance standards.
• Design and scale cross-functional delivery pods as the primary execution engine, combining business analysts, AI engineers, User Experience (UX) designers, and testers — with a hub-and-spoke model leveraging SMBC's Global Capability Centre (GCC) in India for scalable IT delivery and Level 1 / Level 2 (L1/L2) production support.
• Define the delivery operating model end-to-end: intake and prioritisation, solution design sprints, build-test-deploy pipelines, User Acceptance Testing (UAT) protocols, production cutover, hypercare, and steady-state support.
• Establish Continuous Integration / Continuous Deployment (CI/CD) and Machine Learning Operations (MLOps) / Large Language Model Operations (LLMOps) pipelines for automated deployment, versioning, rollback, and monitoring of AI models, agents, and automation workflows.
• Build inner-source libraries, prompt registries, and reusable component catalogues to accelerate delivery velocity and ensure consistency across use cases.

2. End-to-end Design for Enterprise Automation Solutions

• Own the technical solution design for all Asia Pacific AI Cross-Functional Team initiatives — spanning Agentic AI, multi-agent systems, Copilot extensions, intelligent document processing, workflow automation, and predictive analytics.
• Lead solution design using Azure AI Foundry (or equivalent enterprise AI platforms), including model catalogue selection, fine-tuning, prompt flow engineering, grounding with enterprise data via Retrieval-Augmented Generation (RAG) and Graph-based Retrieval-Augmented Generation (GraphRAG), and responsible AI configuration.
• Define and enforce reference architectures, design patterns, and reusable solution accelerators for common use-case archetypes (for example, regulatory scanning, document extraction, customer servicing agents, credit decisioning support).
• Ensure all solution designs meet SMBC's enterprise architecture standards, information security requirements, data governance policies, and regulatory obligations across Asia Pacific jurisdictions.

3. Technology Delivery

• Drive agile, iterative delivery of AI solutions — from rapid prototyping and Minimum Viable Product (MVP) through to production-grade deployment — with a relentless focus on time-to-value and measurable business outcomes (productivity uplift, cost reduction, revenue enablement, risk reduction).
• Track and report delivery Key Performance Indicators (KPIs): sprint velocity, defect rates, deployment frequency, mean time to production, solution adoption rates, and business value delivered (quantified Return on Investment).

Requirements

• 15+ years of technology leadership experience within banking, financial services, or highly regulated industries
• 5+ years of hands-on experience designing and delivering Artificial Intelligence, Generative Artificial Intelligence (Generative AI), and/or intelligent automation solutions in production environments
• Proven experience with enterprise automation platforms (Microsoft Power Platform / Power Automate, UiPath, or equivalent) and their integration with AI and Generative AI capabilities
• Good working knowledge of Azure AI Foundry (or equivalent platforms such as Amazon Web Services Bedrock or Google Cloud Platform Vertex AI) — including model deployment, prompt flow, grounding, fine-tuning, and evaluation tooling
• Strong expertise in cloud-native architectures (Microsoft Azure preferred), data engineering, lakehouse and data mesh patterns, and Application Programming Interface (API)-driven integration
• Hands-on proficiency with Large Language Models (for example, GPT-4, GPT-4o, Claude, open-source models), Microsoft Copilot Studio, prompt engineering, Retrieval-Augmented Generation, Graph-based Retrieval-Augmented Generation, vector databases, and embedding strategies
• Good understanding of one or more Agentic AI frameworks such as AutoGen, Semantic Kernel, LangGraph, CrewAI, or equivalent agent orchestration frameworks, ideally with demonstrable experience designing and deploying Agentic AI and multi-agent systems — including agent orchestration, tool-use, memory, guardrails, and human-in-the-loop patterns
• Familiarity with Large Language Model and Generative AI evaluation frameworks — automated metrics, red-teaming, regression testing, observability, and continuous monitoring in production, ideally with hands-on experience of AI evaluation tooling — Azure AI Evaluation Software Development Kit, Promptflow evaluators, Retrieval-Augmented Generation Assessment (RAGAS), DeepEval, LangSmith, or custom evaluation pipelines
• Experience implementing Continuous Integration / Continuous Deployment pipelines for AI and Machine Learning models, including versioning, A/B deployment, monitoring, and automated rollback
• Understanding of AI-specific security concerns — prompt injection mitigation, data leakage prevention, Personally Identifiable Information (PII) handling, content filtering, and access control in multi-tenant AI deployments
• Proven track record of building and scaling delivery organisations — including offshore/nearshore models, Global Capability Centre leverage, and cross-functional pod structures
• Experience navigating IT risk, Model Risk Management, information security, and regulatory frameworks in multi-jurisdictional Asia Pacific environments
• Significant cross-border experience across Asia Pacific in Banking Technology — Japan and broader Asia experience strongly preferred