Senior Security Research Engineer AI Security

Position Summary

Key Responsibilities

• Conduct in-depth research into AI and LLM-specific threats including prompt injection MCP/A2A vulnerabilities agentic behavior exploits etc.
• Analyze map and expand upon the OWASP LLM Top 10 and GenAI Security categories developing real-world reproductions detections and mitigations.
• Design prototype and evaluate AI detection and protection products including prompt firewalls LLM security filters behavior-based anomaly detectors and AI threat classifiers.
• Study research and document emerging AI attack trends.
• Develop and maintain AI attack simulation frameworks red team automation tools and automated testing pipelines for evaluating LLM and agentic system security.
• Collaborate closely with engineering teams to operationalize AI security research into Traceables product and pipelines.
• Perform red-teaming of LLMs and adversarial evaluations of AI-based agents chatbots and integrations.
• Identify and validate detection signals for AI misuse adversarial activity and data exfiltration across API model and agent layers.
• Contribute to Traceables AI security product roadmap advising on detection capabilities and new defenses.
• Participate in industry collaborations (OWASP CSA GenAI Security working groups) to strengthen the AI security ecosystem.

Required Skills & Experience

• 37 years in security research application security or security engineering.
• Deep understanding of LLM architectures AI/ML pipelines and AI agent frameworks.
• Strong expertise in AI threats especially those outlined in the OWASP LLM Top 10 and beyond.
• Proven experience performing LLM red teaming prompt injection testing or adversarial evaluation of AI systems.
• Experience in mitigating threats in AI and LLM-based systems.
• Proficient in Python with hands-on experience developing proof-of-concept exploits automation tools or detectors.
• Familiarity with OWASP standards including OWASP GenAI Security OWASP LLM Top 10 OWASP API Top 10 OWASP Top 10.
• Demonstrated ability to integrate AI or security research into production-grade detection or protection systems.
• Prior experience building or contributing to automated security testing tools for AI or LLM applications.
• Working knowledge of Java applications and secure coding practices.
• Familiarity with building agentic workflows including attack simulation and detection.
• Experience with vector databases retrieval-augmented generation (RAG) systems and model context communication and storage AI data sharing mechanisms.
• Strong analytical communication and technical documentation skills.
• Working knowledge of Java applications and secure coding principles.

Nice to Have

• Contributions to OWASP AI/LLM Top 10 GenAI Security or open-source AI security projects.
• Research or engineering experience with LLM firewalls AI security middleware.
• Familiarity with LLMOps AI supply chain security and model governance frameworks.
• Background in API security runtime protection detection engineering
• Publications blogs or conference talks on AI/LLM security or adversarial machine learning.