Threat Modeling and Red Teaming Security Engineer

About the Role

We are seeking a highly skilled and motivated AI/ML Security Engineer to join our cutting-edge security team. You will be responsible for ensuring the security and governance of AI/ML models and systems, focusing on AI Governance, Threat Modeling, and Red Teaming. Your expertise will help safeguard critical AI applications from potential threats and vulnerabilities while establishing robust governance practices.

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