AI Security Lead Architect

ARYAN SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

An innovative technology firm in Singapore is seeking an AI Security Lead Architect to oversee the security architecture for AI systems across cloud and on-prem environments. This role is crucial in ensuring that AI platforms comply with enterprise security standards and regulatory frameworks while enabling effective production use. The ideal candidate will have extensive experience in defining secure-by-design standards and conducting thorough AI-specific risk assessments.

Qualifications

• 5+ years of experience in AI security and governance architecture.
• Proven ability to define secure architectures for AI systems.
• Experience in regulatory compliance and emerging AI frameworks.

Responsibilities

• Define secure architecture standards for AI and ML platforms.
• Conduct AI-specific risk assessments including model misuse.
• Embed security controls across data pipelines and model training.

Skills

AI/ML security architectures

Data privacy techniques

Risk assessment methodologies

Penetration testing

Tools

CleverHans

Burp Suite

IBM Adversarial Robustness Toolbox

AWS SageMaker

Job description

Overview

A large enterprise is scaling AI and GenAI across core platforms and requires a AI Security Lead Architect to define how these systems are secured, governed, and trusted.

This role owns the security and governance architecture for AI systems across cloud and on-prem environments, ensuring AI platforms meet enterprise security standards, regulatory obligations, and emerging AI risk frameworks while enabling real production use.

Role scope

• Define secure-by-design architecture standards for AI and ML platforms
• Embed security, privacy, and compliance controls across data pipelines, model training, deployment, and access layers
• Translate regulatory and governance requirements into enforceable technical controls
• Establish Responsible AI and Explainable AI practices covering fairness, transparency, and auditability

• Lead AI-specific risk assessments including model misuse, bias, data leakage, adversarial attacks, and LLM vulnerabilities
• Review and approve AI architectures, platforms, and third-party AI integrations
• Integrate privacy-preserving techniques such as anonymisation, encryption, tokenisation, and secure logging
• Partner with cybersecurity teams to ensure AI systems undergo VAPT and continuous risk monitoring
• Evaluate and select AI security and governance tooling across cloud platforms
• Act as the authority for AI security and governance standards across the organisation

Required experience

Artificial intelligence / machine learning security experience that aligns with the following categories and examples of relevant capabilities. The list below is indicative of the stack and areas of focus.

AI / ML attack & defence

• CleverHans
• Foolbox
• TextAttack
• IBM Adversarial Robustness Toolbox (ART)
• OpenAI Evals
• Red teaming LLMs with custom jailbreak corpora
• Prompt injection testing against tool-calling / function-calling LLMs
• Training data poisoning detection techniques
• Model inversion and membership inference testing

OWASP / security standards

• OWASP Top 10 for Machine Learning
• OWASP LLM Top 10
• STRIDE threat modeling applied to ML pipelines
• AI supply-chain risk analysis (models, datasets, embeddings)

Pen testing / offensive

• Burp Suite against inference APIs
• API fuzzing of model endpoints
• Adversarial payload crafting for LLM tools
• Abuse simulation for agent workflows
• Model misuse scenarios and exploit reproduction

Cloud / platform

• AWS SageMaker IAM, VPC isolation, private endpoints
• Azure ML private links, managed identities
• GCP Vertex AI private service connect
• KMS / HSM integration for model and data protection
• Secure artifact storage for models and embeddings

Kubernetes / runtime

• Kubernetes RBAC hardening
• NetworkPolicies for model isolation
• Secure model serving (KServe, Seldon)
• Admission controllers for AI workloads
• Secrets via Vault / cloud native KMS

Pipeline & ops

• Secure feature stores
• Model registry access control
• CI/CD hardening for ML pipelines
• Canary deployments for models
• Rollback strategies for compromised models

Data & privacy

• Dataset lineage and provenance tracking
• Differential privacy implementation
• PII leakage testing in LLM outputs
• Tokenisation and masking at inference time
• Secure embedding storage and access control

Monitoring

• Prompt and output logging with PII redaction
• Drift detection tied to security events
• Abuse pattern detection in inference traffic
• Explainability tooling used for audit, not demos

Singapore based. Confidential role.