AI Agent Security Research Engineer

Requirements

• Development Experience: 3+ years of backend development experience, proficient in at least one of Python/Go/Java, with a solid engineering foundation.
• Agent Implementation & Security: Hands-on experience deploying LLM Agents (not just demos), capable of detailing engineering challenges such as Agent architecture design, hallucination handling, and tool invocation fault tolerance; Hands-on experience with AI security, understanding risks like prompt injection, jailbreaking, malicious agent injection, and tool misuse, with implementable defense strategies.
• Framework Proficiency: Familiarity with at least one agent framework (LangChain, LlamaIndex, AutoGen, CrewAI, or LangGraph), with production project experience.
• Engineering Capabilities: Proficient in Docker and Kubernetes, with expertise in microservices architecture design and deployment.
• Preferred Qualifications
• Security Tool Experience: Experience with SAST/SCA tools, or deep usage of code auditing tools like CodeQL, Semgrep, or SonarQube.
• Model Fine-Tuning: Experience with LLM fine-tuning (SFT, LoRA), or familiarity with local deployment and optimization of models like Llama 3, Qwen, or DeepSeek. Bonus points for security-domain fine-tuning experience, such as training and evaluating security detection models for malicious prompt detection, unauthorized access identification, or harmful content filtering.
• Open-Source Contributions: High-quality open-source projects related to agents on GitHub, or pull requests submitted to mainstream LLM frameworks.
• Security Competitions: Awards from CTF competitions, or a track record of submitting CVE/CNVD vulnerabilities.

Perks & Benefits