AI-Native Security Agent Engineer

Team Introduction The Network Security team is on the mission to build innovative and enterprise-level network security products and solutions, to protect online businesses from being interrupted by cyber attacks. We’re a group of people passionate about challenging the status quo to solve the real world’s cybersecurity problems effectively and efficiently. We set audacious goals and strive to achieve them. We believe in teamwork and collaboration and we encourage open and succinct communication. If you share the same value and would like to make an impact on network security, please come join us!

Responsibilities

• Architect and implement security features and systems for cloud-native and edge environments (including WAF, Bot Management, and API Security)
• Design and build security capabilities for AI-native systems, covering LLM/Agent abuse prevention, risk detection, and behavior analysis

Artificial Intelligence (AI), Team Player, Network Security, Security Attacks, Internet Security, Linux Media Players, Set Goals, Communication Skills, Cloud Computing, Application Programming Interface (API), * Deeply engage in AI Agent open-source projects, contributing to core capability evolution and driving real-world adoption through community collaborationMinimum Qualifications

• 3+ years of software engineering experience in security, networking, AI, or related domains
• Proficiency in at least one of Go, Python, or TypeScript, with a track record of building complex, scalable systems
• Hands-on experience developing LLM/Agent-based systems (e.g., tool/function calling, context management, or related security implementations)
• Strong communication skills and a self-driven mindset, with the ability to deliver results in fast-paced and ambiguous environments

Preferred Qualifications

• Proven technical contributions to open-source ecosystems (e.g., OpenClaw, Hermes Agent, LLM Guard)
• Practical experience in LLM/AI Security (e.g., Prompt Injection defense, abuse prevention, content safety, or model risk control)