ph3Detection Engineer /h3 pbBring every AI agent out of the box from google and crowdstrike to live. /b /p pJoin a general enterprise's cyber defense function as the engineer who brings machine learning into the detection workflow. The mission: move detection beyond static rules into predictive, model-driven threat identification — surfacing patterns, anomalies, and behavioral signals across EDR and SIEM telemetry. You'll partner closely with detection engineers, threat hunters, and cyber threat intelligence to catch what rule-based systems miss. /p h3What you'll do /h3 ul liDesign, train, and deploy machine learning models for threat detection, with a focus on anomaly detection, behavioral analytics, and user/entity behavior analysis across endpoint and SIEM data sources. /li liBuild and maintain AI-driven detection pipelines capable of continuous learning as new threats emerge. /li liWork with Detection Engineers to turn model outputs into explainable, actionable detection logic that SOC analysts can triage with confidence. /li liOwn feature engineering and training data curation across endpoint and SIEM telemetry, with attention to data quality, labeling accuracy, and statistical relevance. /li liIdentify and mitigate AI-specific risks, including model drift, biased training data, adversarial inputs, and automation over-reliance. /li liStay ahead of the evolving adversarial AI landscape — AI-generated phishing, evasive malware, deepfake-enabled social engineering — and build countermeasures into the detection stack. /li liBring an MLOps discipline to the role: version control for models, production performance monitoring, and ongoing evaluation of precision, recall,



and false-positive rates against team KPIs. /li /ul h3What you'll bring /h3 ul li3+ years applying machine learning to cybersecurity, fraud detection, or large-scale anomaly detection challenges. /li liStrong Python fundamentals and hands‑on experience with ML frameworks such as scikit‑learn, PyTorch, TensorFlow, or XGBoost, spanning both classical and deep learning methods. /li liPractical familiarity with endpoint detection and response tools, SIEM platforms, and an understanding of how detection pipelines consume model output. /li liSolid grounding in feature engineering, model evaluation, and handling class imbalance. /li liExposure to MLOps practices: model versioning, monitoring, and retraining workflows, using tools like MLflow, Vertex AI, or similar. /li liAwareness of adversarial ML risks (model poisoning, evasion techniques, prompt injection) and mitigation approaches in live environments. /li /ul h3What's on offer /h3 ul liHybrid working model balancing in‑office collaboration and remote flexibility, including the option to work abroad for part of the year. /li liPerformance‑based rewards: bonus scheme, pension contribution, employee share program, and additional discounts (varies by location). /li liOngoing career development, learning and training budget. /li liFlexible work arrangements plus health, wellbeing, and family‑related benefits, including support for parental leave and returning from career breaks. /li lib"You run it - you build it" mindset. Take ownership, bring your ideas, improve security inside the company, tackle new challenges and grow in your career inside the company. /b /li /ul /p #J-18808-Ljbffr

📌 Detection Engineer (Madrid)
🏢 Montash
📍 Madrid