AI Cybersecurity Engineer

Responsibilities

• Design, implement, and maintain AI/ML-driven threat detection and response capabilities across cloud, endpoint, identity, and network environments.
• Detect and defend against AI-enabled and AI-targeted attacks, including prompt injection, model poisoning, data leakage, adversarial ML, and automated social engineering.
• Engineer high-fidelity detections across SIEM and security platforms and continuously tune for precision and efficacy.
• Define and implement security controls for AI systems, including model access, training data protection, inference security, and logging.
• Partner with data science and engineering teams to ensure secure AI lifecycle practices.
• Assess and mitigate risks associated with third-party and vendor AI platforms.
• Translate enterprise security policy into enforceable technical controls for AI workloads.
• Map AI security capabilities to frameworks such as NIST CSF, MITRE ATT&CK, MITRE ATLAS, and OWASP AI/LLM Top 10.
• Produce architecture diagrams, security patterns, and implementation guidance suitable for audit and executive review.
• Support incident response involving AI-driven threats or AI system compromise.
• Research emerging AI threat techniques and translate findings into preventive and detective controls.
• Lead proof-of-concept efforts for applying AI to detection engineering and security automation.
• Communicate AI security risks and mitigations to technical teams, risk leaders, and executives.
• Serve as a technical mentor and subject matter expert without formal people management duties.

Requirements

• Bachelor's Degree in Information Technology, Computer Engineering, or related field.
• 7+ years of experience in cybersecurity engineering, detection engineering, or security operations.
• Demonstrated experience in AI or ML techniques in security use cases.
• Strong knowledge of cloud, identity, endpoint, network, and data security.
• Proficiency with SIEM platforms, telemetry pipelines, and detection engineering tools.
• Ability to operate independently and deliver production quality security capabilities.

Preferred Qualifications

• Experience securing large language models (LLMs) or AI platforms in production.
• Familiarity with secure AI development or MLOps security practices.
• Scripting or automation experience (Python, PowerShell).
• Experience in regulated or highly audited environments.