Threat Modelling & Red Teaming Security Engineer

About the Role

AI/ML Security Engineer

Key Responsibilities

• Design and implement AI Governance frameworks to ensure security, compliance, and ethical use of AI models
• Conduct Threat Modeling to identify vulnerabilities within AI/ML pipelines and develop mitigation strategies
• Perform Red Teaming exercises to simulate real-world attacks on AI systems and evaluate their resilience
• Collaborate with data scientists and ML engineers to build secure AI models that resist adversarial attacks
• Develop and maintain security policies and best practices related to AI/ML applications
• Monitor and assess AI model behavior to detect anomalies and potential security breaches
• Stay up-to-date with the latest threats and vulnerabilities specific to AI/ML systems
• Document and communicate findings to stakeholders, including detailed technical reports and recommendations

Skills and Qualifications

Must-Have:

• Proven experience in AI/ML security and governance
• Strong knowledge of AI Governance frameworks and risk management practices
• Expertise in Threat Modeling techniques for AI/ML pipelines
• Proficiency in conducting Red Teaming exercises with a focus on AI systems
• Hands-on experience with Python and libraries related to AI and security (e.g., TensorFlow, PyTorch, Scikit-learn)
• Familiarity with adversarial attack techniques and defense mechanisms in ML models
• Solid understanding of cybersecurity principles and secure coding practices
• Excellent problem-solving skills and the ability to analyze complex security scenarios

Good to Have:

• Experience with ML model explainability and interpretability techniques
• Familiarity with security frameworks like NIST and ISO related to AI/ML governance
• Knowledge of cloud security (AWS, Azure, or GCP) related to AI deployments
• Strong analytical and technical writing skills for documenting findings and solutions