- Develop AI/ML models for intrusion detection, malware classification, phishing detection, insider threat analysis, behavioural analysis, and other related applications.

- Define and execute data science strategies aligned with cybersecurity use cases (e.g., anomaly detection, threat classification, behavioural analytics).

- Collaborate with the product and engineering teams to build end-to-end ML pipelines.

- Stay current and incorporate technological advancements in the areas of adversarial ML, model robustness, and explainable AI in security contexts to uniquely address emerging threats.

- Collecting, curating, and performing exploratory data analysis on large-scale security datasets (from various data sources like threat intel feeds, SIEM events, EDR logs, etc.).

- Continuously monitor and maintain model performance metrics (precision, recall, F1 score, etc.).

- Establish the MLOps best practices and ensure robust model deployment, versioning, and monitoring.

- Optimise and develop tailor-made AI/ML models to work at the edge on-device and at scale on the cloud infrastructure.

Preferred Skills :

- Experienced with ML, graph-based modelling, advanced Gen AI, Agentic AI technologies applied in the cybersecurity areas like intrusion detection, malware classification, phishing detection, insider threat, anomaly detection, emerging AI threat, etc.

- Experience with cybersecurity frameworks like MITRE ATT&CK, MITRE ATLAS, NIST, etc.

- Understanding of data privacy, encryption, secure data handling, etc.

- Deep understanding of statistical modelling, classification, clustering, and time-series forecasting.