Senior AI & Cloud Security Engineer

Role Overview

We are seeking a highly skilled Senior AI & Cloud Security Engineer to join our Singapore-based team. This role involves crafting secure AI applications, cloud infrastructures, and the pipelines integrating them, focusing on cutting-edge AI and cloud security challenges. You will lead technical initiatives for securing generative AI systems, cloud environments, and engineering workflows, emphasizing risk mitigation and the automation of security processes.

Key Responsibilities

• Conduct comprehensive threat modeling and security assessments of large language model (LLM) applications, retrieval-augmented generation (RAG) architectures, and agent-based systems, targeting vulnerabilities like prompt injection, data leakage, and insecure operations.
• Develop and perform AI security testing and adversarial evaluations, collaborating with specialized red teams when needed.
• Ensure the security and integrity of AI and data supply chains, maintaining model provenance, dependencies, vector databases, grounding datasets, and third-party service integrations.
• Integrate security into CI/CD processes using policy-as-code, automate security testing, and manage vulnerabilities in containers and infrastructure-as-code (IaC) assets.
• Architect and maintain comprehensive cloud security controls, including identity and access management, network segmentation, secrets and key lifecycle management, data safeguarding, logging, and audit mechanisms.
• Create automation and evaluation tools with Python or similar programming languages to replace manual security checks with scalable, repeatable engineering controls.
• Serve as a subject matter expert during AI and cloud security incident response, architectural reviews, and engineering remediation efforts, translating emerging threat intelligence into actionable security measures for internal and client teams.

Qualifications and Experience

• Minimum of seven years' hands-on involvement in security engineering disciplines such as application security, cloud security, or DevSecOps, including direct experience safeguarding AI/ML or Generative AI workloads.
• Robust expertise in DevSecOps practices, covering secure CI/CD pipelines, container and Kubernetes security strategies, infrastructure-as-code auditing, vulnerability management, and software supply-chain protections.
• Practical knowledge of modern AI system architectures featuring model APIs, RAG frameworks, vector or retrieval-based systems, orchestration tooling, and AI agents.
• Comprehensive understanding of application and API security fundamentals, identity and access management, workload identities, secrets management, data security, and cloud-native security mechanisms.
• Deep proficiency with at least one major cloud platform such as AWS, Azure, or Google Cloud Platform, with capability to apply consistent security practices across diverse cloud environments.