AI Security Engineer – AI Guardrails / LLM Security

Astek; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Astek seeks an experienced AI Security Engineer to design, implement, and secure an enterprise-wide AI Guardrails platform, supporting AI-enabled applications. The role focuses on centralised, auditable AI security controls using AWS EKS, GPU-enabled infrastructure, and AI Gateway integration.

Responsibilities include deploying AI security infrastructure, conducting threat modelling, and integrating security into DevSecOps and IaC workflows.

Qualifications

• Degree in Computer Science, Information Technology, or a related discipline.
• At least 6 years of cybersecurity, application security, cloud security, platform security, or secure software engineering experience.
• Minimum 2 years hands-on AI security experience in enterprise environments.
• Experience securing LLMs and AI applications (GPT, Claude, Llama, Gemini).

Responsibilities

• Design, implement, and secure an enterprise-wide AI Guardrails platform.
• Deploy AI security infrastructure using AWS EKS, Kubernetes, and GPU-enabled compute environments.
• Establish secure deployment architectures, governance controls, and AI Gateway integrations.
• Conduct AI security assessments, threat modelling, architecture reviews, and AI Red Teaming.
• Collaborate with engineering, cloud, cybersecurity, and AI teams to ensure secure platform delivery.

Skills

Python

REST APIs

DevSecOps

Kubernetes

Security

Education

Bachelor's degree in CS/IT or related

Tools

AWS EKS

Databricks

Job description

We are looking for an experienced AI Security Engineer to design, implement, and secure an enterprise-wide AI Guardrails platform supporting AI-enabled applications.

The role will focus on delivering centralised, consistent, and auditable AI security controls, leveraging AWS EKS, GPU-enabled infrastructure, and AI Gateway integration. The platform will support high-performance model inference, unified administration, and a multi-Availability Zone (Multi-AZ), high-availability architecture.

Key Responsibilities

Design, develop, and implement AI Security Guardrails to protect enterprise AI applications and Large Language Models (LLMs).

Deploy and manage AI security infrastructure using AWS EKS, Kubernetes, and GPU-enabled compute environments.

Establish secure deployment architectures, governance controls, and integration with AI Gateway platforms.

Conduct AI security assessments, threat modelling, architecture reviews, and AI Red Teaming.

Identify and mitigate AI-specific threats, including prompt injection, data leakage, model poisoning, insecure outputs, model theft, supply chain vulnerabilities, and Agentic AI risks.

Assess and secure Agentic AI systems, Model Context Protocol (MCP) integrations, and LLM applications.

Implement AI model scanning, automated security testing, and runtime security controls.

Develop security automation and integrations using Python, REST APIs, and cloud-native technologies.

Integrate security controls into DevSecOps, CI/CD pipelines, and Infrastructure as Code (IaC) workflows.

Produce technical documentation, security assessments, architecture designs, and operational procedures.

Collaborate with engineering, cloud, cybersecurity, and AI teams to ensure secure and scalable platform delivery.

Must-Have Requirements

Degree in Computer Science, Information Technology, or a related discipline.

At least 6 years of experience in Cybersecurity, Application Security, Cloud Security, Platform Security, or Secure Software Engineering.

Minimum 2 years of hands‑on experience in AI Security, Generative AI Security, LLM Security, or AI/ML Security within an enterprise environment.

Experience securing LLMs and AI applications, including models such as GPT, Claude, Llama, or Gemini.

Strong knowledge of AI security threats, AI Guardrails, AI Red Teaming, model scanning, risk assessments, and threat modelling.

Experience securing Agentic AI applications and Model Context Protocol (MCP) ecosystems.

Experience with cloud AI platforms such as AWS Bedrock, AWS SageMaker, Azure AI Foundry/Azure ML, or Google Vertex AI.

Strong programming skills in Python and at least one additional language: Java, Go, C#, or C++.

Good understanding of REST APIs, JSON, authentication protocols, and API security.

Experience with DevSecOps, CI/CD, Infrastructure as Code (IaC), GitHub/GitLab, and automated security testing.

Hands‑on experience with Docker, Kubernetes, and cloud-native deployments.

Knowledge of SAST, DAST, SCA, secrets management, software supply chain security, and secure development practices.

Strong analytical, troubleshooting, documentation, communication, and stakeholder management skills.

Experience in banking, financial services, asset management, or regulated environments.

Experience with Databricks and Databricks AI Security Framework (DASF 2.0).

Knowledge of OWASP Top 10 for LLM Applications, OWASP Agentic AI Security, NIST AI RMF, MITRE ATLAS, and CSA AI Controls Framework.

Exposure to AI security platforms such as Operant AI, HiddenLayer, Protect AI, Prompt Security, Palo Alto Prisma AIRS, or NVIDIA NeMo Guardrails.

Experience with AI Gateway, AI observability, AI governance, model risk management, and LLM evaluation frameworks.

Familiarity with RAG architectures, vector databases, MLflow, Snowflake, and Databricks.

Experience working in Agile, Scrum, DevOps, MLOps, or SAFe environments.

Certifications such as CISSP, CISM, CCSP, CCSK, CEH, GIAC, or relevant Cloud/AI Security certifications.

Key Technologies

AI Guardrails | LLM Security | Agentic AI | MCP | Python | AWS EKS | Kubernetes | Docker | AWS Bedrock | SageMaker | Azure AI Foundry | Vertex AI | AI Gateway | REST APIs | DevSecOps | CI/CD | IaC | GitHub/GitLab | SAST | DAST | SCA | AI Red Teaming | NVIDIA NeMo Guardrails