AI Security Engineer

U3 SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

U3 SOLUTIONS PTE. LTD. is delivering an enterprise AI Guardrails capability to provide centralized, auditable runtime protection for AI-enabled applications. The project uses AWS EKS for control plane and GPU-enabled compute for high-performance model execution, with a unified admin console and a multi-AZ high-availability deployment.

The initiative covers deployment topology, governance, and foundational platform setup with native AI Gateway integration to support enterprise-scale operations.

Qualifications

• Minimum 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, AI application security, or AI/ML security within an enterprise environment.
• Hands-on experience securing AI systems and Large Language Models (LLMs), including GPT, Claude, Llama, Gemini, or equivalent models.
• Strong understanding of AI security risks and controls, including prompt injection, data leakage, model poisoning, insecure output handling, model theft, supply chain risks, and agentic AI threats.
• Hands-on experience with AI Security Guardrails, AI Red Teaming, AI Model Scanning, AI risk assessments, and threat modeling.
• Hands-on experience designing, developing, assessing, or securing Agentic AI systems 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 and scripting skills in Python and at least one additional language such as Java, Go, C#, or C++.
• Strong understanding of REST APIs, JSON, authentication protocols, and API security.
• Experience in conducting security assessments, threat modeling, security architecture reviews, and secure design reviews.
• Experience with DevSecOps practices, CI/CD pipelines, Infrastructure as Code (IaC), GitHub/GitLab, automated security testing, and cloud-native deployments.
• Experience with container and orchestration technologies such as Docker and Kubernetes.

Skills

Cybersecurity

AI Security

AI/ML security

Threat modeling

AI Guardrails

Red Teaming

Model Scanning

MCP ecosystems

Cloud AI platforms

Python

Java

Go

C#

C++

REST APIs

API security

DevSecOps

IaC

GitHub/GitLab

Docker

Kubernetes

Tools

AWS Bedrock

AWS SageMaker

Azure AI Foundry

Google Vertex AI

Job description

Summary

• The project delivers an enterprise AI Guardrails capability to provide centralized, consistent, and auditable runtime protection for AI-enabled applications.
• Deployment of AI Guardrails by leveraging AWS EKS to manage the control plane while hosting the inference data plane on GPU enabled compute for high-performance model execution.
• The solution will include a unified administration console and a multi-AZ, high-availability deployment architecture. Key deliverables encompass the deployment topology, governance framework, and foundational platform setup with native integration with AI Gateway to support enterprise-scale operations.

Skillset Requirements

• Minimum 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, AI application security, or AI/ML security within an enterprise environment.
• Hands-on experience securing AI systems and Large Language Models (LLMs), including GPT, Claude, Llama, Gemini, or equivalent models.
• Strong understanding of AI security risks and controls, including prompt injection, data leakage, model poisoning, insecure output handling, model theft, supply chain risks, and agentic AI threats.
• Hands-on experience with AI Security Guardrails, AI Red Teaming, AI Model Scanning, AI risk assessments, and threat modeling.
• Hands-on experience designing, developing, assessing, or securing Agentic AI systems 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 and scripting skills in Python and at least one additional language such as Java, Go, C#, or C++.
• Strong understanding of REST APIs, JSON, authentication protocols, and API security.
• Experience in conducting security assessments, threat modeling, security architecture reviews, and secure design reviews.
• Experience with DevSecOps practices, CI/CD pipelines, Infrastructure as Code (IaC), GitHub/GitLab, automated security testing, and cloud-native deployments.
• Experience with container and orchestration technologies such as Docker and Kubernetes.
• Knowledge of? .. (some incomplete text?) ... ???