AI Penetration Testing Engineer

Mandatory

• 5+ years of experience in offensive security / penetration testing, with strong web, API, and cloud testing depth.
• Demonstrated hands-on security testing of AI / LLM-based applications (e.g., prompt injection, data leakage, agent and tool abuse).
• Working knowledge of how LLM applications are built: prompts, context windows, embeddings, RAG, agents, and tool calling.
• Strong scripting ability in Python for test automation and tooling.
• Bachelor’s degree in Computer Science, IT, Cybersecurity, or a related field (or equivalent demonstrated experience).

Desirable

• Experience with adversarial ML techniques and ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face).
• Experience with AI platform services on at least one cloud (Azure AI, AWS Bedrock / SageMaker, Google Vertex AI).
• Red-team, purple-team, or detection-aware testing experience.
• Published research, CVEs, conference talks, or responsible-disclosure contributions in AI security.
• Customer-facing delivery, readout, and solutioning experience.

Knowledge areas and standards

• OWASP Top 10 for LLM Applications and OWASP GenAI Security Project guidance.
• MITRE ATLAS (Adversarial Threat Landscape for AI Systems) and MITRE ATT&CK.
• NIST AI Risk Management Framework (AI RMF) and NIST AI 100-2 (adversarial machine learning taxonomy).
• OWASP Top 10 (Web, API), OWASP WSTG, and penetration testing methodologies (PTES, NIST SP 800-115).
• AI regulation and standards awareness: EU AI Act and ISO/IEC 42001.
• CVSS, CWE, and severity rating approaches adapted to AI-specific findings.
• Responsible AI principles and secure AI development lifecycle practices.