AI Cyber Defense Engineer- Remote

Overview

AI Cyber Defense Engineer - 1890

Location: Remote

Experience: 8-15 years overall (2-5 years relevant AI/ML focus)

Role Overview :
We're looking for a mid-level AI Cyber Defense Engineer to support the Cyber Threat Interdiction team. This role focuses on designing and running AI experiments using AWS Bedrock
, with a strong emphasis on secure, cost-aware, and responsible AI development.

You'll work closely with engineers and cyber security professionals to prototype AI-driven solutions that strengthen threat detection and defense capabilities.

• Design, build, and test AI experiments using AWS Bedrock, including model selection, prompt strategies, and orchestration patterns.
• Develop secure Python-based AI and MCP-related components, minimizing risks tied to context sharing and tool invocation.
• Implement and manage human-in-the-loop workflows for validation and approval of AI outputs.
• Support data preparation, pipeline development, and cost monitoring to stay within usage budgets.
• Apply cyber security concepts to shape experiments supporting detection, interdiction, and operational security.
• Document experiment setups, results, risks, and recommendations.
• Contribute reusable templates, runbooks, and guardrails to scale AI experimentation safely.

• 2-5 years of hands-on experience in AI/ML engineering, data engineering, or software engineering.
• Strong experience with AWS Bedrock (model provisioning, API integration, prompt configuration, evaluation).
• Proficiency in Python, including working with LLM APIs and building automation or orchestration tools.
• Experience with AWS services such as Lambda, S3, Cloud Watch, Step Functions, or Dynamo
  
  DB.
• Solid understanding of secure AI development practices, including safe prompt design and data minimization.
• Experience implementing human-in-the-loop validation or approval workflows.
• Exposure to cyber security domains such as detection engineering, incident response, threat intelligence, or security operations.

• Experience with RAG architectures, vector databases, or embeddings.
• Familiarity with responsible AI frameworks or model safety evaluations.
• Background in building AI prototypes or proof-of-concept tools.
• Knowledge of secure coding practices and adversarial AI risks.