AI Security Expert

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.