About Us
Mizuho Bank is the banking subsidiary of Mizuho Financial Group, which is headquartered in Tokyo, Japan. Mizuho Financial Group, Inc. is the 15th largest bank in the world, as measured by total assets of approximately US 2 trillion. Mizuho's 55,000 employees worldwide offer comprehensive financial services to clients in over 800 offices throughout the Americas, EMEA, and Asia.
Mizuho Bank Singapore Branch has an established local presence with over 50 years of history and serves as the regional hub for the bank's APAC operations.
Operating with a Full Bank License, Mizuho Bank Singapore Branch provides banking services to over 2,000 Japanese and non Japanese corporate clients, with a staff strength of about 1,000 employees. We provide expertise in corporate finance, trade finance, cash management, funds transfers, project finance, and treasury services to help businesses develop and find new opportunities. We also collaborate with our affiliate company, Mizuho Securities, to provide investment banking solutions to our clients.
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2083BR
Department
Asia-Pacific Corporate Function Coordination Department
Section
Transversal Delivery Infra & Operations
Location
Singapore
Job Responsibilities
As an AI Tech Lead, you will be the driving force behind the design, deployment, and governance of enterprise-scale AI and GenAI solutions in a secure, high-compliance banking environment. You will lead a multidisciplinary team to architect robust, explainable, and ethical AI systems, balancing innovation with operational risk. Your expertise will shape both production-grade, on-premise AI platforms and agile, cloud-based prototypes on Azure, ensuring seamless transition from experimentation to enterprise deployment.
Key Responsibilities
Enterprise GenAI Solution Leadership
Lead design and development of AI and GenAI solutions for banking use cases through prototypes, pilots and MVPs. Translate complex AI concepts into actionable business outcomes.
Hybrid Infrastructure Management
Lead the secure deployment and orchestration of AI and GenAI models on-premise using Kubernetes, GPU clusters, and secure networking for production. Manage Azure-based environments for non-production experimentation and rapid prototyping.
AI Platform Integration and Governance
Integrate GenAI and machine learning pipelines with legacy banking systems and modern cloud infrastructure. Enforce responsible AI guardrails, explainability, and regulatory compliance throughout the lifecycle.
Provide Technical Leadership
Mentor and guide engineers and data scientists through end-to-end AI solution delivery, from requirements gathering to production deployment and ongoing monitoring. Building organisational capabilities : Deisgn and deliver training programs, workshops, and hands-on lab for IT teams across the bank.
Continuous Learning
Evaluate emerging AI and GenAI frameworks, architectures and automation techniques. Promote a culture of experimentation and responsible adoption.
Key Competencies
Strategic architecture
Ability to design end-to-end AI and GenAI solutions that integrate seamlessly with legacy banking systems and modern cloud infrastructure.
Deep AI and GenAI Expertise
Hands-on experience with advanced AI and GenAI frameworks and orchestration tools relevant to large-scale enterprise deployment.
Hybrid Infrastructure and MLOps
Proficient with Kubernetes, Docker, GPU orchestration, MLflow, Kubeflow, Databricks, and Azure Machine Learning. Skilled at managing secure, compliant AI workloads across on-premise and cloud environments.
Technical Leadership
Proven track record of mentoring senior engineers and driving agile development cycles for complex, high-stakes projects.
Risk and Compliance Mindset
Strong understanding of AI ethics, explainability, bias mitigation, and data privacy laws in financial services.
Organizational Capability Building
Experience designing and delivering training, workshops, and hands-on labs for technical teams.
Job Requirements
Educational Background and Experience:
- Minimum Bachelor's degree in Computer Science, Information Technology, Statistics, Mathematics, or a related quantitative field.
- Experience: Minimum of 10+ years in software engineering, with at least 4 years dedicated to leading AI/ML initiatives in a corporate or fintech environment.
- Demonstrated experience architecting, deploying, and governing enterprise GenAI solutions.
- Proven track record managing hybrid on-premise and cloud AI infrastructure.
Skill Set Requirements
Required Technical Skills
Programming Expert proficiency in Python, with experience applying best practices for robust, scalable, and maintainable code in AI and GenAI projects.
AI and GenAI Frameworks
Deep hands-on experience with leading machine learning and GenAI frameworks for both model development and deployment. This includes traditional ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn) as well as GenAI frameworks and orchestration tools suitable for large-scale, enterprise use.
Infrastructure and Orchestration
Proven ability to design, deploy, and manage secure, scalable AI and GenAI solutions across hybrid environments. Deep familiarity with orchestration and workflow automation tools for distributed model training and inference.
MLOps and Model Lifecycle Management
Strong experience with MLOps practices, including automated CI/CD pipelines, model versioning, monitoring, and governance for both AI and GenAI systems. Familiarity with tools such as MLflow, Kubeflow, and Databricks.
Data Engineering
Proficient in building and optimizing data pipelines for high-compliance environments, including ETL orchestration, NoSQL databases, and large-scale data processing platforms.
Cross-functional Skills
Regulatory and Ethical Navigation
Demonstrated ability to work with Legal and Compliance teams to ensure all AI and GenAI initiatives meet evolving regulatory, ethical, and governance standards. Skilled in bias management, data privacy, explainability, and responsible AI practices.
Financial Domain Knowledge
Solid understanding of financial products, market dynamics, and operational risk, with the ability to tailor AI and GenAI solutions to the unique requirements of banking and capital markets.
Stakeholder Engagement and Communication
Strong capability to translate complex technical concepts into clear, actionable business outcomes for a range of stakeholders, including senior management, business units, and non-technical teams.
Change Management and Organizational Enablement
Experience leading cultural and process transformation, enabling traditional banking teams to adopt AI-driven workflows and fostering trust in automated, data-driven decision-making.
