Senior AI & Cloud Security Engineer

Tap Growth ai; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Tap Growth ai is seeking a Senior AI & Cloud Security Engineer to design, implement, and secure AI and cloud solutions. The role focuses on threat-modeling, automating security checks, and integrating security into CI/CD pipelines.

You will work with engineering teams to identify risks, establish practical controls, and ensure safe production deployment of AI workloads across major cloud environments. This is a senior individual-contributor role requiring technical leadership.

Qualifications

• 7+ years of hands-on security engineering experience.
• Strong DevSecOps experience covering CI/CD security and container security.
• Hands-on understanding of AI architectures and security implications.
• Knowledge of application and API security, IAM, and data protection.
• Deep expertise on AWS/Azure/GCP or equivalent cloud platforms.

Responsibilities

• Threat-model LLM applications, RAG architectures, and agentic workflows.
• Design AI security testing and adversarial evaluations with red-team support.
• Secure AI/data supply chains including provenance and dependencies.
• Embed security into CI/CD with policy-as-code and automated checks.
• Design cloud security controls across identity, networks, and secrets.
• Build security automation tooling and incident SME for AI/cloud workloads.

Skills

Security engineering

DevSecOps

Cloud security

AI security

CI/CD security

Python

Tools

Kubernetes

Terraform

Job description

We're Hiring: Senior AI & Cloud Security Engineer!

Join our team as a Senior AI & Cloud Security Engineer and play a key role in designing, implementing, and securing advanced cloud and AI solutions. We are looking for an experienced professional who is passionate about cybersecurity, cloud architecture, and emerging AI technologies to ensure the integrity and resilience of our systems.

What You'll Do:

About the role - “Senior AI & Cloud Security Engineer”

We are looking for a senior hands-on security engineer to secure AI applications, cloud platforms, and the engineering pipelines that connect them.

This role combines AI security, cloud security, application security, and DevSecOps, with a strong focus on LLM, RAG, and agentic workloads. You will work directly with engineering teams to identify risks, design practical controls, automate security checks, and help AI solutions move safely into production.

This is a senior individual-contributor role focused on technical leadership and delivery, not people management.

• Threat-model and security-review LLM applications, RAG architectures, and agentic workflows, including risks such as prompt injection, data leakage, insecure tool use, and excessive agency
• Design and execute AI security testing and adversarial evaluations, working with specialist red-team teams where appropriate
• Secure AI and data supply chains, including model and artifact provenance, dependencies, vector stores, grounding data, and third-party integrations
• Embed security into CI/CD through policy-as-code, automated security testing, container and IaC scanning, and vulnerability management
• Design and implement cloud security controls across identity, network segmentation, secrets and key management, data protection, logging, and auditability
• Build security automation and evaluation tooling using Python or equivalent languages, replacing manual checks with repeatable engineering controls
• Act as a technical SME for AI and cloud security incidents, architecture reviews, and engineering remediation, while translating emerging threats into practical controls for internal and client teams

What you bring

• 7+ years of hands-on experience in security engineering, application security, cloud security, DevSecOps, or related roles, with practical experience securing AI/ML or Generative AI workloads
• Strong DevSecOps experience covering CI/CD security, container and Kubernetes security, infrastructure-as-code, vulnerability management, and software supply-chain security
• Hands-on understanding of modern AI architectures including model APIs, RAG, vector/retrieval systems, orchestration frameworks, and AI agents
• Strong knowledge of application and API security, IAM/workload identity, secrets management, data protection, and cloud-native security controls
• Deep expertise in at least one major cloud platform such as AWS, Azure, or GCP, with the ability to apply equivalent security patterns across cloud environments