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 Traceable’s 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 Traceable’s 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

• 3–7 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.