GenAI Security & Quality Engineer (AI Assurance)

Capgemini; Singapore; On-site; SGD 90,000 - 130,000; Full time

Benefits offered by this job

Flexible work practices

Learning & development

Job summary

Capgemini Singapore is seeking a highly technical Software Engineer to strengthen the security, compliance, and quality assurance of our enterprise-grade Generative AI platform.

The role focuses on AI security assurance, including Moonshot and Litmus testing, risk-based assessments of LLM and agentic applications, and translating governance requirements into practical controls while supporting reliable product delivery.

Qualifications

• 3+ years of software engineering, cybersecurity, or AI assurance experience.
• Understanding of AI security risks in LLM/RAG contexts (prompt injection, data leakage).
• Hands-on experience with AI evaluation tools and red-teaming.
• Ability to translate security and governance requirements into controls and tests.
• Experience reviewing data flows and cloud architectures for secure designs.
• Strong stakeholder communication to guide secure implementations.
• Familiarity with AWS, containers, databases, encryption, and data handling.

Responsibilities

• Design and execute AI security assessments for LLM, RAG, and agentic apps.
• Implement Moonshot and Litmus testing across AI use cases.
• Develop test data governance for functional, safety, and security evaluation.
• Translate security policies into testable controls and evidence.
• Integrate security tests into CI/CD pipelines and release gates.
• Build and maintain automated functional, regression, API, and end-to-end tests.

Skills

AI security testing

Moonshot

Litmus

Compliance & risk advisory

Secure architecture

Security automation

Test data governance

Tools

Moonshot

Litmus

Job description

Capgemini Singapore is seeking a highly technical Software Engineer to strengthen the security, compliance, and quality assurance of our enterprise-grade Generative AI platform.

The role focuses on AI security assurance, including Moonshot and Litmus testing, risk-based assessments of LLM and agentic applications, and translating governance requirements into practical controls while supporting reliable product delivery.