AI Security Lead

Strong proficiency in Python with experience building security evaluation scripts and adversarial testing utilities.
Knowledge of adversarial ML techniques including evasion, poisoning, extraction, inversion, and prompt manipulation methods.
Familiarity with adversarial testing frameworks and libraries (e.g., TextAttack, IBM ART, PyRIT, MITRE ATLAS tools).
Understanding of ML and DL model behavior, architecture vulnerabilities, and LLM‑specific attack vectors.
Experience with red‑team style testing for LLMs, RAG systems, and multimodal AI systems.
Knowledge of MLOps/LLMOps workflows and integrating security tests into CI/CD evaluation pipelines.
AI Security Evaluations: Conduct structured adversarial testing, vulnerability analysis, and security assessments on AI models, datasets, and pipelines to identify technical weaknesses. 2. Design AI Security Testing Methodologies: Develop evaluation strategies, attack simulations, and test cases that examine model behavior under adversarial and stress conditions. 3. Analyze Model Vulnerabilities: Review model outputs, logs, and error patterns to detect indicators of poisoning, evasion, extraction attempts, or unsafe behavior requiring mitigation. 4. Support Development of AI Defense Mechanisms: Contribute technical insights for designing and implementing guardrail components, safety filters, validation controls, and secure model behavior constraints. 5. Conduct Research on Emerging AI Threats: Study new attack surfaces such as prompt injection, model inversion, indirect influence attacks, and supply chain vulnerabilities relevant to modern AI systems. 6. Build Security Testing Scripts and Pipelines: Create and maintain scripts, automation tasks, and evaluation pipelines that streamline security testing, red team simulations, and model behavior monitoring. 7. Document AI Security Findings: Prepare technical reports, vulnerability summaries, experiment results, and evidence artifacts that support security reviews and R&D decision-making. 8. Collaborate with Cross Functional Teams: Work with engineering, Responsible AI, cybersecurity, and data science teams to jointly investigate issues and integrate necessary security improvements. 9. Secure Model Deployment Activities: Assist in validating the security posture of models before deployment by verifying guardrail integration, access controls, and vulnerability remediation. 10. Monitor Emerging AI Security Tools and Techniques: Continuously evaluate new defensive methods, adversarial testing libraries, and AI security research to enhance testing capabilities and inform tooling upgrades.

Work arrangement
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