Principal Detection Engineer
- 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.
- 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).
Information Technology
Job Level:
TCP_05 States with Pay Range Requirement The expected salary/wage range for a U.S. -based hire filling this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level. If this is a sales role, then the listed salary range reflects combined base salary and target-level sales compensation pay. If this is a non-sales role, then the listed salary range reflects base salary only. Variable incentives may also be offered. Information about employee benefits offered can be found at . USD Annual Salary: $117,500.00 - $270,000.00 HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity . Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities. HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.
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