Headquartered in Tokyo, Sumitomo Mitsui Banking Corporation (SMBC) is a leading global financial institution and a core member of Sumitomo Mitsui Financial Group (SMBC Group). Built upon our rich Japanese heritage since 1876, we put our customers first and provide seamless access to, from and within the Asia Pacific region. SMBC is one of the largest Japanese banks by assets and maintain strong credit ratings across our global integrated network. We work closely as one SMBC Group to offer personal, corporate and investment banking services to meet the needs of our customers.

With sustainability embedded within our strategy and operations, we are committed to creating a society in which today’s generation can enjoy economic prosperity and well-being, and pass it on to future generations.

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

Key Competencies

• Strategic technology leadership with the ability to set technical vision, make high-stakes architecture decisions, and align technology delivery with business strategy at the executive level
• AI solution architecture expertise in designing enterprise-grade AI solutions — from Large Language Model selection through agent orchestration to production deployment and monitoring
• Evaluation and quality rigour with an instinctive commitment to measurable quality — drives systematic evaluation, testing, and continuous improvement of AI outputs and system behaviour
• Execution excellence with relentless focus on delivery — breaks down complex programmes into executable sprints, removes blockers, and drives teams to ship production-grade solutions at pace
• Strong governance and risk management acumen — balances innovation speed with compliance, security, and responsible AI obligations across complex regulatory landscapes
• Cross-cultural influence and collaboration — proven ability to lead and influence across cultures, geographies, and organisational boundaries, particularly across Japan, Singapore, and broader Asia Pacific
• Executive communication — translates complex technical concepts into compelling business narratives for C-suite, Board, and regulatory audiences