AI Cybersecurity Engineer

Job Summary

Required Qualifications

• 5+ years of experience in cybersecurity engineering, with at least 2 years of hands-on experience in AI/ML security, GenAI systems, agentic platforms, and LLM application development.
• Deep understanding of Zero Trust architecture principles (NIST SP 800-207) and hands-on experience implementing controls in cloud-native or hybrid environments.
• Hands-on experience with cloud security in at least one major cloud platform (AWS, Azure, or GCP), including cloud-native IAM, Cloud Security Posture Management (CSPM), and cloud AI service security controls.
• Experience implementing Data Loss Prevention (DLP) controls within AI pipelines; familiarity with data residency requirements and privacy-preserving techniques (e.g., tokenization, redaction) as applied to GenAI workflows.
• Demonstrated experience securing LLM-based applications, including prompt injection defenses, output validation, and responsible AI guardrails.
• Hands-on experience building or securing RAG pipelines, including vector database access control and retrieval-layer security.
• Familiarity with agentic AI frameworks (LangChain, LangGraph, AutoGen, CrewAI, or equivalent) and the security risks associated with autonomous multi-agent systems.
• Strong Python proficiency; experience with ML frameworks (PyTorch, TensorFlow, Hugging Face transformers) and security data pipelines.
• Experience with SIEM/SOAR platforms (Rapid7, Microsoft Sentinel) and integrating AI capabilities into security operations workflows.
• Working knowledge of IAM, OAuth 2.0 / OIDC, and secrets management (HashiCorp Vault, AWS Secrets Manager, Secret Server) in the context of AI system authentication.
• Familiarity with MITRE ATT&CK, MITRE ATLAS (adversarial threats to AI/ML systems), and OWASP LLM Top 10.
• Excellent communication skills; able to translate complex AI security risks for executive, legal, and non-technical audiences.
• Experience developing and executing incident response procedures specific to AI systems, including response plans for model compromise, agent misbehavior events, and data exfiltration through LLM outputs.
• Demonstrated ability to author enforceable security policies and standards, including acceptable use frameworks, data classification guidelines, and AI security control baselines applicable across engineering and business teams.