Description

You will develop and deploy machine learning models to enhance threat detection and automate response systems across enterprise infrastructure.

Responsibilities

• Design and implement machine learning models to detect malware, phishing, intrusion, and APTs.
• Analyze large datasets from logs, endpoints, and network traffic to identify anomalies.
• Automate threat detection and response workflows using AI/ML tools.
• Develop User and Entity Behavior Analytics (UEBA) models for early threat identification.
• Integrate AI/ML models into existing SIEM, SOAR, or XDR platforms.
• Ensure AI systems are explainable, auditable, and compliant with data privacy standards.

Required Skills

• 3+ years of experience in cybersecurity engineering or threat detection.
• 2+ years of hands-on experience with machine learning and data science in a security context.
• Proficiency in Python and ML libraries including TensorFlow, PyTorch, and Scikit-learn.
• Experience with SIEM platforms such as Splunk or Elastic.
• Strong understanding of security operations, threat intelligence, and SOC workflows.
• Working knowledge of Linux and Windows operating systems.
• Solid understanding of networking and common attack vectors.
• Experience with log analysis tools and data lakes.

Preferred Skills

• Experience with NLP techniques for phishing and email threat detection.
• Familiarity with cloud security (AWS, Azure, GCP) and SOAR tools.
• Knowledge of adversarial machine learning and model robustness testing.
• Security certifications such as CISSP, CEH, OSCP, or GIAC.

Key Skills

Education

Any Graduate