AI Security Architect

GAC Solutions; Jersey City (NJ); On-site; USD 140,000 - 200,000; Full time

Job summary

GAC Solutions in Jersey City, NJ seeks a security architect to design and review secure architectures for AI/ML platforms, LLM applications, and secure deployment pipelines. You'll embed security into MLOps, CI/CD, and IaC, and collaborate with engineering to implement risk-based controls.

You will conduct threat modeling for prompt injection, data leakage, and model risk; manage IAM, encryption, and logging; review third-party models and tools for privacy and compliance; and build monitoring

Qualifications

• 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.
• Strong AWS cloud security exposure or comparable hyperscaler security depth, including IAM, encryption, network controls, logging, secrets, and secure deployment patterns.
• Understanding of AI/ML and GenAI-specific risks such as prompt injection, adversarial attacks, data leakage, model misuse, retrieval poisoning, model supply-chain risk, and unsafe tool use.
• Familiarity with threat modeling, vulnerability management, security testing, incident response, secure SDLC, DevSecOps, and Terraform/IaC controls.

Skills

Cybersecurity

Cloud security

Application security

DevSecOps

Threat modeling

Incident response

Security testing

Tools

Terraform

Kubernetes

AWS

CI/CD tooling

Job description

• 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, unauthorized access, and third-party model risk.
• Implement controls for AWS IAM, encryption, key management, 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, Terraform modules, and deployment pipelines.
• Review cloud-agnostic IaC templates and AWS-specific deployments for least privilege, secure defaults, segregation of duties, policy compliance, and auditability.
• Review third-party models, APIs, open-source packages, AI tools, and vendor platforms for security, privacy, model supply-chain, 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, remediation planning, and production-readiness reviews.
• Maintain audit-ready documentation for controls, testing, risk acceptance, remediation, and production approvals.

Must-have experience

• 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.
• Strong AWS cloud security exposure or comparable hyperscaler security depth, including IAM, encryption, network controls, logging, secrets, and secure deployment patterns.
• Understanding of AI/ML and GenAI-specific risks such as prompt injection, adversarial attacks, data leakage, model misuse, retrieval poisoning, model supply-chain risk, and unsafe tool use.
• Familiarity with threat modeling, vulnerability management, security testing, incident response, secure SDLC, DevSecOps, and Terraform/IaC controls.
• Ability to work directly with engineering teams to implement practical, risk-based controls.