About this role
TEK-Engineers
Actively Hiring
Save
Apply Now
Cloud Security (Vulnerability) - Remote Role
- $130k – $140k
- |Remote ( United States )
- |8 years of exp
- |Contract
Posted: yesterday• Recruiter recently active
Hires remotely in
United States
Remote Work Policy Remote only
Company Location
Dallas
Visa Sponsorship Not Available
Preferred Timezones Central Time, Eastern Time
Collaboration Hours 8:00 AM - 5:00 PM Eastern Time
RelocationAllowed
About the job
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