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AI Security Engineer

LinkedIn Crowe Atlanta, GA
Not Applicable Posted March 30, 2026 9 variants Job link
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Requirements
  • Mentoring junior engineers, ML engineers, and security practitioners on AI security best practices.
  • Contributing to cloud security posture management capabilities for AI-enabled platforms.
  • 4+ years of experience in cybersecurity, cloud security, ML engineering, or DevSecOps roles.
  • Demonstrated experience securing AI/ML or generative AI systems in production environments.
  • Strong understanding of ML pipelines, model architectures, and AI system components.
  • Deep knowledge of adversarial ML attack vectors and mitigation techniques.
  • Proficiency in Python, security testing tools, and cloud security frameworks.
  • Ability to assess risk across distributed services, storage systems, inference APIs, and data pipelines.
  • Strong communication skills and sound technical judgment in security decision-making.
  • Hands-on experience with Microsoft Azure and M365 security environments.
  • Willingness to travel occasionally for cross-functional planning and collaboration.
Preferred Skills
  • Master’s degree or advanced training in cybersecurity, AI, or related discipline.
  • Security and cloud certifications such as SC-100, SC-900, SC-200, SC-300, AZ-500, AI-102, or equivalent AWS certifications.
  • CISSP, CKS, or CompTIA Cloud certifications.
  • Advanced experience securing AI platforms on Azure, including Kubernetes security (RBAC, network policies) and multi-tenant GPU workloads.
  • Experience securing container pipelines using image scanning, signing, and policy enforcement.
  • Expertise with secrets management solutions (e.g., Azure Key Vault, HashiCorp Vault).
  • Experience implementing zero-trust architecture and securing CI/CD pipelines for AI systems.
  • Deep knowledge of generative AI and RAG security, including prevention of prompt injections, jailbreaks, context poisoning, and embedding leakage.
  • Experience designing safe-output rendering patterns, guardrails, and red-teaming processes for generative systems.
  • Familiarity with emerging generative AI defense techniques such as model watermarking, inference integrity checks, and output validation frameworks.
Education
  • (Not required) – Bachelor’s degree in Cybersecurity, Computer Science, Engineering, or a related technical field, or equivalent experience.
  • (Not required) – Master’s degree or advanced training in cybersecurity, AI, or related discipline.
  • (Not required) – Security and cloud certifications such as SC-100, SC-900, SC-200, SC-300, AZ-500, AI-102, or equivalent AWS certifications.
  • (Not required) – CISSP, CKS, or CompTIA Cloud certifications.