Application Security Architect

ADECCO PERSONNEL PTE LTD; Singapore; On-site; SGD 100,000 - 150,000; Full time

Job summary

A technology consulting firm in Singapore is seeking an Application Security Architect. This role involves conducting security assessments of AI/ML systems, developing security controls, and designing secure cloud architectures for AI workloads. Ideal candidates should have 3–8+ years in cybersecurity and demonstrable experience securing AI/ML platforms. Strong cloud security knowledge across AWS, Azure, and GCP is essential. Communication skills are vital as you will present technical risks to stakeholders.

Qualifications

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

Responsibilities

• Conduct comprehensive security assessments of AI/ML systems.
• Develop and implement security controls and governance frameworks.
• Design secure cloud architectures for AI workloads.

Skills

Cybersecurity

Cloud security

Data security

AI/ML platforms

Container platforms (Docker, Kubernetes)

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

Analytical skills

Communication skills

Tools

AWS

Azure

GCP

Job description

Job Summary A global leader in technology consulting and digital transformation, our client delivers innovative solutions that enhance business performance and resilience. With deep expertise in cloud computing, data analytics, and AI-driven insights, they help organizations modernize systems, improve scalability, and accelerate operational efficiency. Their end-to-end approach to enterprise transformation empowers companies across industries to innovate with confidence and stay competitive in a rapidly evolving digital landscape. Our client is looking for an Application Security Architect responsible for AI/ML related matters. Candidates would have the opportunity to play a key role in security assessment while working with various stakeholders.

Key Responsibilities

• 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, 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 teams to deliver secure AI solutions.

Qualifications and Profile

• 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.