Description

You will leverage artificial intelligence and machine learning to enhance threat detection, automate response systems, and build intelligent security frameworks.

This role is on-site.

Responsibilities

• Design and implement machine learning models to detect cybersecurity threats including malware, phishing, intrusion, and APTs.
• Analyze large datasets from logs, endpoints, and network traffic to identify patterns and anomalies.
• Automate threat detection and response workflows using AI/ML models and tools.
• Develop behavior analytics (UEBA) models to profile users and entities for early threat identification.
• Integrate AI/ML models into existing SIEM, SOAR, or XDR platforms.

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 such as TensorFlow, PyTorch, and Scikit-learn.
• Experience with SIEM platforms like Splunk or Elastic and working with data lakes.
• Strong understanding of security operations, threat intelligence, malware analysis, and SOC workflows.
• Solid understanding of networking, Linux, and Windows operating systems.
• Knowledge of common attack vectors.

Preferred Skills

• Experience with NLP techniques for phishing and email threat detection.
• Familiarity with cloud security (AWS, Azure, GCP) and security automation tools.
• Knowledge of adversarial machine learning and model robustness testing.

Key Skills

Education

Any Graduate