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AI Cloud Security Engineer

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Remote

AI Cloud Security Engineer

Optomi

AI Cloud Security Engineer

Skills

CybersecurityAIAWS

1 month ago

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Important information


Contract type:

Freelance / Contract

Location:

Dallas, TX, USA

Work mode:

Remote

Published on:

23 September 2026

What they need


Optomi, in partnership with a leading provider in the Airline industry is seeking an AI Cloud Security Engineer to join their team. In this role, you will be responsible for implementing and operationalizing AI security capabilities across the organizations cloud-hosted AI platforms, with primary responsibility for AWS Bedrock Security Guardrails, CrowdStrike AIDR for Agents, and frontier model security controls. The resource will design, deploy, validate, and continuously improve preventive, detective, and governance controls for Generative AI and Agentic AI workloads hosted within enterprise cloud environments.

You will leverage AI security best practices from AWS, NIST AI RMF, OWASP for LLM Applications, and MITRE ATLAS to integrate AI-specific security controls into existing cloud engineering and cybersecurity operating models. This individual must be capable of moving from architecture and design discussions to hands-on implementation, automation, testing, deployment, and operational support of AI security controls.

WORK ACTIVITIES/CONTEXT:

  • Configure and maintain AI security controls for enterprise GenAI and Agentic AI solutions deployed within AWS environments.
  • Implement AWS Bedrock Guardrails, including content filtering, harmful content protection, sensitive information detection, and model access controls.
  • Partner with platform and engineering teams to establish AI guardrails as code and embed AI security controls into CI/CD pipelines.
  • Deploy and operationalize CrowdStrike AIDR capabilities for AI workloads and agents.
  • Deploy and operationalize runtime protection, monitoring, and detection capabilities for AI applications, foundation models, and AI agents.
  • Develop and execute security testing for LLM and Agentic AI workloads, including prompt injection testing, tool abuse testing, sensitive data leakage testing, and validation of response controls.
  • Configure logging, telemetry, and observability capabilities for AI services and support integration of monitoring data into security operations platforms.
  • Partner with Cloud Security, Operations, Platform Engineering, Application Development teams to operationalize AI security monitoring and response capabilities.
  • Support implementation of model access controls, token protection controls, output filtering mechanisms, and PII redaction capabilities.

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