AI Security & Compliance Engineer

Title: AI Security & Compliance Engineer

Location: Jersey City NJ (Hyrbid)

Type: Contract

Primary ownership

• Security architecture and control implementation for AI platforms LLM applications RAG pipelines and agentic systems.
• Threat modeling AI red teaming vulnerability assessment and remediation guidance.
• Security evidence control documentation and compliance support for production approvals.
• Key responsibilities
• Design and review secure architectures for AI/ML platforms LLM applications RAG pipelines model-serving environments and agentic AI workflows.
• Conduct threat modeling for prompt injection jailbreaks insecure tool use model inversion data leakage retrieval poisoning adversarial inputs and unauthorized access.
• Implement controls for IAM encryption secrets management network segmentation API security logging secure data handling and data-loss prevention.
• Embed security into MLOps LLMOps CI/CD container security infrastructure-as-code and deployment pipelines.
• Review third-party models APIs open-source packages AI tools and vendor platforms for security privacy and compliance risks.
• Build monitoring and alerting for suspicious AI usage anomalous access policy violations unsafe interactions and potential data leakage.
• Support AI red teaming penetration testing vulnerability management incident response and remediation planning.
• Maintain audit-ready documentation for controls testing risk acceptance and production-readiness reviews.

Must-have candidate profile:

• Strong background in cybersecurity cloud security application security DevSecOps or technology risk.
• Experience securing cloud-native platforms APIs microservices containers Kubernetes CI/CD pipelines and infrastructure-as-code.
• Understanding of AI/ML and GenAI-specific risks such as prompt injection adversarial attacks data leakage model misuse and unsafe tool use.
• Familiarity with threat modeling vulnerability management security testing incident response and secure SDLC practices.
• Ability to work directly with engineering teams to implement practical risk-based controls.
• Preferred experience
• Experience securing AI/ML platforms or GenAI applications in production.
• Financial-services security technology risk regulatory or audit experience.
• Familiarity with AI red teaming model supply-chain risk secure RAG design LLM gateways and privacy-by-design controls.
• Initial screening questions
• How would you threat-model a RAG system that accesses confidential enterprise documents
• What controls reduce prompt injection and data leakage risks
• How do you secure an AI model-serving pipeline from source to production
• What AI-specific red-team tests have you designed or executed