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AI Summary
The engineer will design, develop, and optimize AI-security-specific threat models, tools, and solutions for threat identification, prediction, and prevention. This includes implementing and securing machine learning and GenAI models to enhance threat detection and integrating these tools across cybersecurity architectures.
The Cyber AI Engineer will drive the development and optimization of advanced tools, processes, and workflows to predict, detect, and prevent AI-specific threats. This hands-on technical leadership role combines deep research in AI security with close collaboration across teams to integrate AI security capabilities into robust cybersecurity architectures
Key Responsibilities:
• Design, develop, and optimize AI-security-specific threat models, tools, and solutions for threat identification, prediction, and prevention.
• Implement and secure machine learning models, GenAI models, and AI techniques to enhance threat detection, monitoring, and risk scoring.
• Integrate AI security tools and technologies across cybersecurity architectures, collaborating with data scientists, security engineers, and other stakeholders.
• Analyze AI security incident data to refine and improve AI models and methodologies.
• Provide technical leadership and mentorship to junior engineers in AI and machine learning.
• Ensure alignment and compliance with industry standards (NIST AI-RMF, ISO 42001, OWASP Top 10 for LLMs) and advanced security architectures (Agentic, MCP).
• Stay abreast of emerging trends and advancements in AI and cybersecurity.
Required Qualifications:
• A Bachelor’s or Master’s degree in Computer Science, Engineering, or a closely related discipline is required.
• 5+ years of experience in AI-focused cybersecurity in an enterprise environment.
• Expertise in Python, R, Java, or similar programming languages.
• Deep understanding of machine learning, neural networks, and application to security systems.
• Hands-on experience with AI security technologies (intrusion detection, anomaly detection, threat intelligence).
• 3+ years’ experience in Azure or AWS cloud-native services, architectures, and tools.
• Expertise in enterprise architectures (including cloud-native and AI architecture patterns).
• Advanced knowledge of security and governance frameworks (NIST AI-RMF, ISO 42001, OWASP Top 10 for LLM).
• Strong communication and collaboration skills.
Preferred Qualifications
• Experience with implementing OWASP Top 10 LLM Threats in practice with any industry or open-source product.
• Working experience in Threat Modeling
• Experience with agentic and Model Context Protocol (MCP) architectures.
• Demonstrated ability to lead cross-functional technical teams.
• Track record of published research or thought leadership in AI security.
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