Title: Security Engineering Vulnerability Protect Engineer
Location: Remote
About the Role
We''re seeking an experienced Security Engineering Vulnerability Protect Engineer to lead the deployment, administration, and strategic use of the HiddenLayer platform in defense of our AI/ML systems. This role sits at the intersection of cybersecurity engineering and applied machine learning, focused on protecting models and LLM-based applications from adversarial attacks, data poisoning, model theft, and other emerging AI-specific threats. You''ll partner closely with Data Science, MLOps, and broader security teams to build a resilient, well-governed AI security posture across the organization.
Responsibilities
Platform Operations
• Deploy, configure, and administer the HiddenLayer platform
• Integrate HiddenLayer with enterprise SIEM, SOAR, EDR, vulnerability management, and cloud security platforms
• Create detection rules, dashboards, and executive reporting on AI security posture
AI/ML Threat Protection
• Protect AI models against adversarial attacks, model theft, prompt injection, model poisoning, and unauthorized inference
• Develop monitoring and detection strategies for production AI workloads
• Conduct AI threat modeling exercises
• Respond to AI-related security incidents and perform root cause analysis
Security Assessment & Governance
• Assess AI applications for security risks across the full development lifecycle
• Design governance around AI model inventory, risk classification, and lifecycle management
• Document architecture, standards, and operational procedures
Cross-Functional Partnership
• Partner with Data Science and MLOps teams to implement secure model deployment pipelines
• Stay current on emerging AI attack techniques and defensive capabilities Required Qualifications
• 5+ years of experience in cybersecurity engineering or security architecture
• 2+ years supporting AI/ML security initiatives
• Hands-on experience deploying or administering HiddenLayer
• Working knowledge of adversarial machine learning techniques, including:
• Prompt injection
• Model extraction
• Data poisoning
• Model evasion
• Membership inference
• Supply chain attacks
• Experience securing LLM-based applications
• Understanding of AI model lifecycle management
• Familiarity with Python and REST APIs
• Experience with Kubernetes and container security
• Knowledge of AI services on AWS, Azure, or Google Cloud Platform
• Experience integrating security platforms via APIs and automation
Preferred Qualifications
• Experience with additional AI security platforms: Protect AI, Microsoft AI Security, NVIDIA AI Enterprise Security, or Palo Alto AI Runtime Security
• Experience with ML platforms/tools: MLflow, Kubeflow, SageMaker, Vertex AI, or Azure Machine Learning
• Experience with SIEM platforms: Splunk, Microsoft Sentinel, Google Chronicle, or QRadar
• Security certifications: CISSP, GSEC, GIAC, or cloud security certifications
• Familiarity with AI governance frameworks: NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, or MITRE ATLAS
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