Key Responsibilities:

- Design, develop, and deploy AI/ML models for cybersecurity applications.

- Research emerging AI techniques and their application to threat detection, malware analysis, fraud detection, and anomaly detection.

- Develop predictive models to identify and mitigate cyber threats.

- Analyze large-scale security datasets, logs, and network traffic to uncover attack patterns.

- Build intelligent automation for incident detection, investigation, and response.

- Collaborate with Security Operations Center (SOC), threat intelligence, engineering, and product teams to integrate AI-powered security solutions.

- Evaluate the effectiveness of AI models and continuously improve accuracy, performance, and scalability.

- Stay current with advancements in artificial intelligence, cybersecurity, and adversarial machine learning.

- Conduct experiments, publish research findings where appropriate, and contribute to innovation initiatives.

- Ensure AI systems comply with security, privacy, and regulatory requirements.

Required Qualifications:

- Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Cybersecurity, Data Science, or a related field.

- 3+ years of experience in AI/ML research or development with a focus on cybersecurity.

- Strong programming skills in Python and experience with AI/ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.

- Knowledge of cybersecurity concepts including malware analysis, network security, endpoint security, identity security, and threat intelligence.

- Experience with deep learning, natural language processing (NLP), graph machine learning, or reinforcement learning.

- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.

- Experience working with large datasets, distributed computing, and data engineering tools.

- Strong analytical, problem-solving, and communication skills.

Preferred Qualifications:

- Experience with Large Language Models (LLMs), AI agents, retrieval-augmented generation (RAG), and generative AI security.

- Knowledge of adversarial machine learning and AI model security.

- Experience with SIEM, SOAR, EDR, and XDR platforms.

- Familiarity with MITRE ATT&CK, Zero Trust architecture, and threat modeling.

- Security certifications such as CISSP, GSEC, CEH, or GIAC are a plus.

- Publications, patents, or open-source contributions related to AI or cybersecurity.

Technical Skills:

- Programming: Python, SQL, C++, Java

- AI/ML: PyTorch, TensorFlow, Scikit-learn, Hugging Face

- Data: Pandas, NumPy, Spark

- Cloud: AWS, Azure, Google Cloud

- Security: SIEM, SOAR, IDS/IPS, EDR, Threat Intelligence

- DevOps: Docker, Kubernetes, Git, CI/CD

Key Competencies:

- AI research and innovation

- Cybersecurity expertise

- Statistical analysis and data modeling

- Critical thinking and problem-solving

- Collaboration and communication

- Continuous learning and adaptability

Success Metrics:

- Improved threat detection accuracy and reduced false positives.

- Faster incident detection and response times.

- Successful deployment of scalable AI-driven security solutions.

- Measurable improvements in cybersecurity resilience through AI innovation.