Asks for

The ad says“Adversarial AI Knowledge: Proven experience with adversarial machine learning techniques, such as Gradient-based attacks, Evasion attacks, and Model Extraction.”
The ad says“Cloud Security: Deep experience with security configurations in AWS, Azure, or GCP, specifically regarding managed AI services (e.g., SageMaker, Vertex AI).”
The ad says“Cloud Security: Deep experience with security configurations in AWS, Azure, or GCP, specifically regarding managed AI services (e.g., SageMaker, Vertex AI).”
The ad says“Secure Software Development: Expertise in securing CI/CD pipelines and containerized environments (Docker, Kubernetes) specifically for ML workloads.”
The ad says“Data Protection: Proficiency in privacy-preserving technologies such as Differential Privacy, Homomorphic Encryption, or Federated Learning.”
The ad says“Secure Software Development: Expertise in securing CI/CD pipelines and containerized environments (Docker, Kubernetes) specifically for ML workloads.”
The ad says“Adversarial AI Knowledge: Proven experience with adversarial machine learning techniques, such as Gradient-based attacks, Evasion attacks, and Model Extraction.”
The ad says“Data Protection: Proficiency in privacy-preserving technologies such as Differential Privacy, Homomorphic Encryption, or Federated Learning.”
The ad says“Cloud Security: Deep experience with security configurations in AWS, Azure, or GCP, specifically regarding managed AI services (e.g., SageMaker, Vertex AI).”
The ad says“Adversarial AI Knowledge: Proven experience with adversarial machine learning techniques, such as Gradient-based attacks, Evasion attacks, and Model Extraction.”
The ad says“Data Protection: Proficiency in privacy-preserving technologies such as Differential Privacy, Homomorphic Encryption, or Federated Learning.”
The ad says“Secure Software Development: Expertise in securing CI/CD pipelines and containerized environments (Docker, Kubernetes) specifically for ML workloads.”
The ad says“AI/ML Proficiency: Strong understanding of machine learning frameworks (e.g., PyTorch, TensorFlow) and the underlying mathematics of model architectures.”
The ad says“Adversarial AI Knowledge: Proven experience with adversarial machine learning techniques, such as Gradient-based attacks, Evasion attacks, and Model Extraction.”
The ad says“AI/ML Proficiency: Strong understanding of machine learning frameworks (e.g., PyTorch, TensorFlow) and the underlying mathematics of model architectures.”
The ad says“Cloud Security: Deep experience with security configurations in AWS, Azure, or GCP, specifically regarding managed AI services (e.g., SageMaker, Vertex AI).”
The ad says“AI/ML Proficiency: Strong understanding of machine learning frameworks (e.g., PyTorch, TensorFlow) and the underlying mathematics of model architectures.”
The ad says“Cloud Security: Deep experience with security configurations in AWS, Azure, or GCP, specifically regarding managed AI services (e.g., SageMaker, Vertex AI).”

Position: AI SECURITY EXPERT

Salary range: up to ₱150,000 Philippine Peso (with the final offer subject to the client’s discretion based on the candidate’s experience)

Working Hours: AU/NZ business hours

Work set-up: 100% Remote work

Holidays: New Zealand Holidays

COMPANY OVERVIEW

Our company is a leading-edge workforce and asset management platform dedicated to resourcing seamlessly at scale. We empower industries with mobile workforces—such as construction, healthcare, and logistics—by streamlining complex scheduling, compliance, and safety workflows through intelligent automation.

OVERVIEW ON THE ROLE

The AI Security Expert bridges the gap between cybersecurity and machine learning to protect AI systems from emerging threats while ensuring models are secure, compliant, and resilient. You will proactively manage risks unique to AI environments—such as adversarial attacks and data poisoning—to build trustworthy, production-ready systems that are robust against an evolving cyber threat landscape.

Core Responsibilities

Your focus will be on the end-to-end security of our AI lifecycle, specifically targeting the following areas:

Pipeline Security: Designing and implementing protocols to secure model training and deployment pipelines against unauthorized access or tampering.
Adversarial Defense: Proactively identifying and preventing adversarial attacks, including input manipulation, model inversion, and data poisoning.
Intellectual Property & Privacy: Implementing advanced measures to protect sensitive dataset privacy and safeguard our proprietary model intellectual property.
Threat Modeling & Red Teaming: Conducting AI-specific threat modeling and internal "red team" exercises to discover vulnerabilities before they can be exploited.
Governance & Compliance: Ensuring all AI initiatives align with global security standards (e.g., ISO/IEC 42001, NIST AI RMF), ethical guidelines, and emerging AI governance frameworks.

Qualifications & Technical Skills

Process thinking and systems design
Problem solving and analytical skills
Strong stakeholder communication
Strategic thinking with practical implementation ability
Ability to translate technical solutions for non-technical teams

Outcomes / Success Measures

Success in this role will be measured by:

Reduction in manual processes
Increased operational efficiency
Successful deployment of AI-powered workflows
Improved data-driven decision making
Scalable automation infrastructure

Requirements

Required

To excel in this role, you will need a unique blend of traditional cybersecurity expertise and deep learning proficiency.

AI/ML Proficiency: Strong understanding of machine learning frameworks (e.g., PyTorch, TensorFlow) and the underlying mathematics of model architectures.
Adversarial AI Knowledge: Proven experience with adversarial machine learning techniques, such as Gradient-based attacks, Evasion attacks, and Model Extraction.
Secure Software Development: Expertise in securing CI/CD pipelines and containerized environments (Docker, Kubernetes) specifically for ML workloads.
Data Protection: Proficiency in privacy-preserving technologies such as Differential Privacy, Homomorphic Encryption, or Federated Learning.
Cloud Security: Deep experience with security configurations in AWS, Azure, or GCP, specifically regarding managed AI services (e.g., SageMaker, Vertex AI).

Education

Education: A Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Data Science, or a related field.
Professional Background: 5+ years of experience in Cybersecurity, with at least 2 years specifically focused on AI/ML security or research.
Certifications: Relevant certifications such as CISSP, CISM, or specialized AI certifications (e.g., Certified AI Security Professional) are highly regarded.