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.

Responsibilities

• Develop and implement security measures specifically tailored for AI/ML models and systems across lifecycle (data, training, deployment, monitoring).
• Integrate security controls within AI development pipelines (MLOps), ensuring secure model training, deployment, and operation.
• Identify vulnerabilities in AI models.
• Conduct threat modeling and risk assessments for AI systems.
• Collaborate with data scientists/specialists, AI engineers and CyberSecurity team to embed security best practices.
• Monitor deployed AI systems for anomalies, attacks and misuse.
• Ensure compliance with data protection and AI governance framework.
• Develop incident response plans tailored to AI-specific threats and coordinate response activities.
• Stay up to date on emerging threats in AI security and contribute to internal security guidelines.

Requirements

• Bachelor's or Master's degree in Computer Science, CyberSecurity, AI/ML, or related field.
• 7-10 years of working experience in relevant field with at least 5 years of AI specific experience.
• Experience in cybersecurity and machine learning/AI systems.
• Strong programming skills (Python, Java or similar).
• Knowledge of:
• Machine learning / AI frameworks
• Security principles (encryption, authentication, network security)
• Cloud platforms (Microsoft Azure, AWS, GCP)
• Understanding of common AI/ML vulnerabilities and attack vectors.
• Experience with adversarial machine learning techniques.
• Familiar with privacy-preserving methods.
• Knowledge of secure software development lifecycle.
• Certifications such as CISSP, CEH or cloud security certifications.
• Experience in regulated industries (finance, healthcare, government).
• Familiarity with regulatory frameworks and standards such as the MAS AI Risk management , EU AI Act, NIST AI Risk Management Framework, or equivalent.

Key skills

• Threat modelling for AI systems.
• Secure ML pipeline design.
• Risk assessment and mitigation.
• Incident detection and response.
• Cross-functional collaboration.
• Analytical and problem solving skills.