Position Overview
The AI Cloud Security Engineer will implement and operationalize enterprise security capabilities across Airlines' cloud-hosted AI platforms. This role is primarily responsible for establishing preventive, detective, and governance controls for Generative AI and Agentic AI workloads hosted on AWS, with an emphasis on AWS Bedrock Security Guardrails, CrowdStrike AIDR for Agents, and frontier model security.
The ideal candidate bridges architecture design and hands-on automation, integrating frameworks such as NIST AI RMF, OWASP Top 10 for LLM Applications, and MITRE ATLAS into enterprise CI/CD pipelines and cloud security operating models.
Key Responsibilities
AI Platform Security & Guardrails: Design, configure, and maintain security controls for GenAI and Agentic AI solutions in AWS. Deploy AWS Bedrock Guardrails, including content filtering, sensitive information redaction (PII), token protection, and model access controls.
Runtime Protection & Detection: Implement and operationalize CrowdStrike AIDR (AI Threat Detection and Response) and runtime protection for foundation models, agents, and cloud workloads.
Security Testing & Red Teaming: Develop and execute adversarial security tests targeting LLMs and AI agents (e.g., prompt injection, jailbreaking, tool abuse, and data exfiltration).
Security as Code & CI/CD Integration: Partner with platform and engineering teams to deploy guardrails-as-code using Terraform or CloudFormation, embedding automated AI security checks into deployment pipelines.
Observability & SIEM Integration: Configure logging, telemetry, and monitoring across AI services (using CloudWatch, OpenTelemetry, etc.) to pipe alerts into enterprise SOC platforms.
Architecture & Governance Alignment: Conduct threat modeling and security reviews for RAG architectures, agent orchestration frameworks, and tool integrations aligned with NIST AI RMF and MITRE ATLAS.
Required Skills & Experience (Prioritized)
- AWS AI & Bedrock Security (5+ years Cloud, 1–2+ years AI Security): Direct hands-on experience configuring AWS Bedrock Guardrails, model access policies, content moderation filters, and foundation model protections.
- AWS Cloud Engineering & IaC: Strong proficiency with AWS core security (IAM, SCPs, AWS Organizations) and Infrastructure as Code (Terraform or CloudFormation).
- Runtime Protection & EDR/AIDR: Proven experience deploying and managing CrowdStrike Falcon, CrowdStrike AIDR, or equivalent cloud runtime detection platforms.
- GenAI / LLM / Agentic AI Security: Deep technical understanding of RAG architectures, AI agent frameworks, prompt injection mitigations, output validation, and token abuse prevention.
- AI Vulnerability Testing: Practical experience conducting security assessments and penetration tests specific to LLMs, tool integrations, and data leakage scenarios.
- CI/CD & DevSecOps: Experience integrating automated security gates and policy-as-code into continuous integration/continuous deployment pipelines.
- Cloud Logging & Observability: Expertise in aggregating AI telemetry via Amazon CloudWatch, SIEM tools, or OpenTelemetry.
Qualifications & Education
Education: Bachelor’s degree in Cybersecurity, Computer Science, Information Systems, Data Science, Engineering, or equivalent practical experience. Advanced degree preferred.
Preferred Certifications
AWS Certified Security – Specialty
AWS Certified AI Practitioner or AWS Cloud Practitioner
CrowdStrike Falcon Platform Certifications (e.g., CCFA, CCFR)
Industry Security Certifications: CISSP, CCSP, or GIAC Cloud Security (GCSA/GPCS)
Professional & Interpersonal Competencies
Proven ability to translate complex AI cybersecurity risks into actionable roadmaps for both engineering teams and senior leadership.
Collaborative mindset with experience working in Agile, cross-functional delivery teams.
Strong critical-thinking skills to analyze emerging AI threat vectors and quickly adapt defense controls.
