AI-Powered Threat Hunter

1 day ago

India beBeeCybersecurity Full time US$ 90,000 - US$ 1,20,000
Job Title:

Cybersecurity Threat Detection Specialist

About the Role:

This is an exciting opportunity to join a team pioneering AI-driven SOC workflows. We are seeking a forward-thinking Cyber Security SOC Analyst with expertise in security operations and a passion for applying large language models (LLMs) to real-world threat detection.

Key Responsibilities:
  • Monitor, triage, and investigate alerts from security tools and platforms.
  • Identify root causes of security incidents and recommend remediation.
  • Differentiate true positives from false positives to refine detection accuracy.
AI & Threat Modelling:
  • Build and evaluate cybersecurity models leveraging LLMs to identify emerging threats.
  • Design and run real-world evaluations of model performance, including adversarial scenarios.
  • Research and recommend data sources for training and fine-tuning models.
  • Develop workflows for data curation, labelling, and threat detection.
  • Document findings, processes, and evaluation metrics.
Data & Evaluation:
  • Perform data labelling for supervised evaluations and model improvement.
  • Establish metrics for reducing false positives and improving precision/recall.
  • Collaborate with security engineers, researchers, and data scientists to operationalize AI-driven SOC workflows.
Qualifications:

We require:

  • 5+ years of experience in a SOC analyst, incident response, or related cybersecurity role.
  • Strong knowledge of alert triage, root cause analysis, and incident handling.
  • Familiarity with security tools (SIEM, EDR, IDS/IPS, etc.).
  • Experience or strong interest in AI/ML applications in cybersecurity.
  • Ability to work independently in part-time PST shifts (every two days).

We prefer:

  • Hands-on experience with LLMs (e.g., fine-tuning, prompt engineering, or evaluation).
  • Familiarity with Python and data manipulation libraries.
  • Background in threat intelligence, malware analysis, or adversarial testing.
  • Understanding of data curation, annotation, and evaluation methods.

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