Vulnerability Protect Engineer

Security Engineering Vulnerability Protect Engineer

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 GCP
• 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