Principal Detection Engineer developing advanced threat detection systems leveraging ML/AI at Hewlett Packard Enterprise. Leading efforts in cybersecurity to mitigate risks with innovative strategies.
Responsibilities
Design, develop, and implement advanced threat detection systems leveraging ML/AI techniques to identify malicious activity, anomalies, and emerging risks
Build and optimize machine learning models for real-time detection, including supervised, unsupervised, and reinforcement learning approaches
Data engineering and pre-processing for cybersecurity applications
Analyze large-scale datasets to extract meaningful insights, detect patterns, and enhance the accuracy of detection systems
Develop and refine detection algorithms for intrusion detection, anomaly detection, endpoint security, behavioral analysis, and other cybersecurity applications
Automate detection workflows and processes to improve efficiency and scalability of security monitoring systems
Work closely with threat intelligence, red team, security operations, and data scientists, to integrate detection models into security platforms and tools
Test, validate, and monitor the performance of detection models, ensuring reliability and minimizing false positives/negatives
Stay up to date with emerging threats, ML/AI technologies, and advancements in cybersecurity to continuously improve detection systems
Maintain clear documentation of models, processes, and methodologies for knowledge sharing across teams.
Requirements
Bachelor’s or master’s degree in computer science, cybersecurity, data science, or related engineering field
Certifications such as CISSP, CISM, CEH or OSCP preferred
Proven experience (8+ years) in cybersecurity, with a focus on threat detection and response
Deep understanding of cybersecurity frameworks and concepts, including attack vectors, threat landscapes, and defense mechanisms
Familiarity with SIEM/SOAR/ and EDR/XDR platforms
Strong expertise in Machine Learning (ML) and Artificial Intelligence (AI), including model design, training, and deployment
Knowledge of adversarial machine learning and techniques for defending against model exploitation
Experience with anomaly detection, behavioral Modeling, and predictive analytics in cybersecurity contexts
Experience with deep learning architectures or natural language processing (NLP) applied to cybersecurity
Experience integrating machine learning models into security operations workflows in enterprise environments
Proficiency in languages such as Python, Go, SPL, YaraL, R , Java, SQL and frameworks like TensorFlow, PyTorch, or Scikit-learn
Hands-on experience with big data technologies and cloud environments (AWS, Azure, GCP)
Familiarity with regulatory requirements and compliance frameworks (e.g., GDPR, NIST, ISO 27001).
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