Automate discovery, and adversarial testing on generative AI, RAG pipelines, and ML systems to identify vulnerabilities such as prompt injection, jailbreaking, and data poisoning.
Provide guidance on secure design, logging, monitoring, and compensating controls for AI applications and platforms.
Define and implement AI red teaming methodologies, playbooks, and success metrics, establishing mechanisms for continuous testing and safe rollout of new AI models and features.
Work with platform and cloud security teams to ensure secure infrastructure configuration and alignment with enterprise security architecture.
Engage with external researchers, vendors, and standards bodies to track emerging AI threats and bring best practices into the organization.
Foster a team culture of diversity, opportunity, inclusion, and respect.
Collaborate within a cross-functional team to develop relationships, influence senior stakeholders, and drive alignment on AI risk tolerance and mitigation priorities.
Required Qualifications, Capabilities, and Skills
Formal training or certification in Public Cloud environment concepts and advanced hands-on experience with cloud-native AI services (e.g., Bedrock).
Practical experience with Infrastructure as Code (IaC) solutions like Terraform and CloudFormation.
Proficiency in Python scripting.
Strong understanding of AI/ML concepts and trends, with knowledge of AI red teaming foundational concepts to design and implement exercises for complex AI architectures.
Ability to conceptualize, design, validate, and communicate creative technical solutions to enterprise-level security problems, including building internal tools, dashboards, and automation for red teaming activities.
Preferred Qualifications, Capabilities, and Skills
Expertise in planning, designing, and implementing AI red teaming exercises and enterprise-level security solutions for generative AI, LLMs, and ML systems.
Experience with specialized AI security/red teaming tools and frameworks (e.g., PyRIT, Garak, custom LLM evaluation harnesses) and contributions to AI security or open-source security projects.
Experience with threat modeling, discovery, vulnerability, and penetration testing (e.g., MITRE ATLAS, OWASP Top 10 for LLMs) and foundational cybersecurity concepts such as IAM, Authentication, OIDC, SAML.
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