Cybersecurity AI Research Engineer

Responsibilities:

• Design AI models for cybersecurity, agentic AI security, pattern recognition, neural networks, NLP/LLMs, GenAI, algorithm research, and software development; integrate AI into security products.

Requirements:

• Must-Have Skills: Agentic AI security, Python, PyTorch/TensorFlow, LLMs, RAG, LangGraph/CrewAI/AutoGen, NLP, ML, software engineering, cybersecurity fundamentals.
• Preferred Security Knowledge: EDR/XDR, SIEM, SOC, Threat Detection, Malware Analysis, Detection Engineering, MITRE ATT& CK, Cloud Security, IAM.
• Experience: 5-9+ years in AI/ML with cybersecurity exposure or security research.
• Education: B. E. /B. Tech. /M. Tech. /MS in CS/AI/Data Science/Cybersecurity-Suitable Profiles AI security researcher, ML engineer (security), applied AI engineer, detection ML engineer, and GenAI security engineer.
• Hiring Strategy: Target AI engineers with security interests or security engineers with strong.
• GenAI skills: Search GitHub, Kaggle, Hugging Face, papers, and DEF CON/ Nullcon speakers and search keywords.
• Skills are GenAI Security, LLM Security, Agentic AI, AI Research Engineer, ML Security, Detection Engineer ML, NLP Engineer, Applied Scientist AI, RAG, LangGraph, CrewAI, PyTorch Interview Focus on Python coding, ML fundamentals, LLM architecture, prompt injection, agent security, RAG evaluation, secure coding, threat modelling, algorithm design, and research aptitude.

Skills

cybersecurity, python, pytorch, security, tensorflow