Cloud Security (Vulnerability) - Remote Role
Summary
6+ month remote security engineering role deploying and administering the Hidden Layer platform to protect AI/ML systems and LLM applications from adversarial attacks, integrating it with SIEM/SOAR/EDR and cloud security tooling. Requires Python, REST APIs, Kubernetes, and cloud AI services on AWS, Azure, or GCP.
Compensation: $130k – $140k
Job Title: Security Engineering Vulnerability Protect Engineer
Location: Remote
Duration: 6+ Months
Responsibilities
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 Hidden Layer 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 Hidden Layer platform
Integrate Hidden Layer 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 Hidden Layer
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
Skills
- AI
- API
- Automation
- AWS
- Azure
- Cissp
- Cloud
- Cloud Security
- Container Security
- Cybersecurity
- Data Science
- EDR
- GCP
- Kubeflow
- Kubernetes
- LLM
- Machine Learning
- Microsoft Sentinel
- MLflow
- MLOps
- Model Deployment
- Nist
- OWASP
- Python
- REST
- Root Cause Analysis
- SageMaker
- SIEM
- Splunk
- Threat Modeling
- Vertex AI
- Vulnerability Management