Detection Engineer - AI/ML

Experteer Overview

You’ll curate feature data from CrowdStrike Falcon and Google SecOps, while managing AI risks and staying ahead of adversarial threats. Embracing an MLOps mindset, you’ll version, monitor, and continually evaluate model performance against KPIs to drive impactful security outcomes.

• Design, train, and deploy ML models for threat detection (anomaly and behavioral analytics) across EDR and SIEM telemetry
• Build and maintain AI-powered detection pipelines that adapt to new threats
• Collaborate with Detection Engineers to translate model outputs into actionable, explainable detections
• Engineer features and curate training data from CrowdStrike Falcon and Google SecOps with emphasis on data quality and labeling
• Monitor and mitigate AI-specific risks (drift, bias, adversarial inputs, over-reliance on automation)
• Track adversarial AI landscape and embed countermeasures into the detection stack
• Apply an MLOps mindset: model versioning, production monitoring, and KPI-based evaluation (precision, recall, FPR)

• 3+ years of hands-on ML in cybersecurity, fraud detection, or large-scale anomaly detection
• Strong Python skills and experience with ML frameworks (scikit-learn, PyTorch, TensorFlow, or XGBoost)
• Working knowledge of security telemetry (EDR: CrowdStrike Falcon; SIEM: Google SecOps/Chronicle) and model-output workflows
• Solid feature engineering, model evaluation, and handling class imbalance
• Familiarity with MLOps practices (model versioning, monitoring, retraining; MLflow, Vertex AI, or equivalent)
• Awareness of adversarial ML threats and defensive measures in operational settings
• Clear English communication to explain model behavior to data scientists and SOC analysts

• hybrid work model
• bonus scheme
• pension
• employee shares program
• employee discounts
• healthcare and parental leave benefits