Security Specialist (AI/ML) (6 months extendable) (Ref 26305)

Jobline Resources Pte Ltd; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A technology consulting firm is seeking an experienced professional for a role focused on AI/ML Security Assessments and Risk Management in Singapore. You will conduct security assessments, implement AI governance frameworks, and design secure cloud infrastructures. The ideal candidate has 3-8+ years of experience in cybersecurity and is well-versed in securing AI systems across various cloud platforms. Relevant certifications and strong communication skills are essential for client interactions and cross-functional teamwork.

Qualifications

• 3–8+ years of experience in cybersecurity or cloud security roles.
• Experience securing AI/ML platforms, models, or systems.
• Strong knowledge of IAM, network security, and API security.

Responsibilities

• Conduct security assessments of AI/ML systems including data pipelines.
• Develop security controls and policies for AI lifecycle management.
• Design secure cloud architectures for AI workloads.

Skills

Cybersecurity

Cloud Security (AWS, Azure, GCP)

Identity & Access Management (IAM)

Data Security

Communication Skills

Education

Certifications like CCSP, CISSP, AWS/Azure/GCP Security Specialty

Tools

Docker

Kubernetes

AI governance tools

MLOps tools (SageMaker, Vertex AI, Azure ML, MLflow)

Job description

Responsibilities

AI/ML Security Assessments & Risk Management

• Conduct comprehensive security assessments of AI/ML systems, including data pipelines, model training environments, inference endpoints, and MLOps workflows.
• Identify complex risks related to data privacy, data leakage, adversarial attacks, model poisoning, prompt injection, and misuse of AI technologies.
• Evaluate threats across the AI lifecycle—from data collection to model retirement—and define appropriate mitigation actions.

AI Governance & Security Controls

• Develop and implement security controls, governance frameworks, and policies for end-to-end AI lifecycle management.
• Support clients in complying with AI regulations, responsible AI principles, and data protection requirements (e.g., GDPR, NIST AI RMF). Create strategic roadmaps and executive-level recommendations for secure AI adoption.

Cloud & Infrastructure Security for AI

• Design secure cloud architectures for AI workloads across AWS, Azure, and GCP. Implement best practices for IAM, encryption, secrets management, container security, network segmentation, and secure data storage.
• Assess and secure APIs, microservices, and application components that support AI models and intelligent systems.

Identity & Access Management for AI Agents

• Design IAM models for AI agents, including agent identities, delegated permissions, temporary credentials, and cross-system trust boundaries.
• Implement zero-trust principles for agent authentication, authorization, and privilege controls. Develop patterns for scoped access, JIT (Just-In-Time) authorizations, short-lived tokens, and decoupled privilege elevation.
• Integrate IAM systems with AI agent orchestration and establish access governance processes, including permission reviews, certifications, and usage monitoring.

Client Communication & Advisory

• Translate technical security risks into clear business impacts that executive stakeholders can act on. Prepare assessment reports, recommendations, threat models, and remediation plans for clients.
• Work cross-functionally with AI engineers, data scientists, IT security, and compliance teams to deliver secure AI solutions.

Requirements

• 3–8+ years of experience in cybersecurity, cloud security, or data security roles.
• Demonstrated experience securing AI/ML platforms, models, pipelines, or agent-based systems. Strong knowledge of cloud security (AWS, Azure, GCP), IAM, network security, encryption, and API security.
• Understanding of AI threats such as adversarial ML, data contamination, and model theft. Experience with container platforms (Docker, Kubernetes) and MLOps tools (SageMaker, Vertex AI, Azure ML, MLflow).
• Excellent analytical and communication skills, with the ability to present findings to technical and non-technical audiences.
• Certifications such as CCSP, CISSP, CCIE, AWS/Azure/GCP Security Specialty, or AI governance credentials.
• Experience in responsible AI, AI policy, or AI compliance frameworks.
• Background in security engineering, threat modeling, or red teaming for AI systems.
• Experience working in consulting organizations or large enterprise security programs.