The posting
Security Test Engineer (OT Security & AI/ML Integration)
Job Summary
We are seeking a Security Test Engineer who will play a critical role in validating attack scenarios generated by Red Team/Hackers and preparing high-quality datasets for the AI/ML defense team. This role ensures that cyberattack behaviors in Operational Technology (OT) environments are reproducible, testable, and measurable, providing the foundation for robust detection and defense mechanisms.
Responsibilities
- Attack Scenario Validation: Collaborate with Red Team to reproduce penetration test and exploit scenarios in controlled OT testbeds, ensuring attack execution is safe, repeatable, and well-documented.
- Data Capture & Management: Collect logs, PCAPs, syscalls, and telemetry from OT devices during attack and normal operations while validating completeness and integrity of datasets. Apply labeling (malicious vs. benign events) for Machine Learning (ML) training.
- Test Automation: Develop automated test cases/scripts to replay attack behaviors and build reusable testing frameworks for continuous validation of AI/ML detection models.
- Quality Assurance for AI/ML Inputs: Define acceptance criteria for datasets (e.g., coverage, signal-to-noise ratio) and deliver curated, clean, and structured data pipelines to AI/ML engineers.
- Regression & Continuous Testing: Re-run attack scenarios against updated defense models, document false positives/negatives and provide structured feedback.
- Collaboration & Reporting: Act as a bridge between Hackers and the AI/ML team, ensuring shared understanding of attack patterns, and reporting gaps, anomalies, and risks in detection coverage.
Qualifications
- Strong knowledge of cybersecurity testing methodologies (penetration testing, fuzzing, adversarial simulation).
- Familiarity with OT/ICS protocols (Modbus, DNP3, OPC-UA, BACnet, etc.).
- Proficiency in data capture tools (Wireshark, Zeek, Suricata, tcpdump).
- Experience with test automation frameworks (Python, Robot Framework, PyTest, or similar).
- Understanding of AI/ML data requirements (structured datasets, labeling, class balance, regression testing).
- Familiarity with CI/CD pipelines for continuous testing.
- Strong documentation and reporting skills.
- Relevant educational qualifications such as a degree in Computer Science, Cybersecurity, or a related field.
Preferred Skills
- Exposure to Red Team/Blue Team engagements.
- Knowledge of adversarial ML (evasion, poisoning attacks).
- Hands-on experience in cloud-based ML pipelines (AWS SageMaker, Azure ML, etc.).
- Certifications such as OSCP, GICSP, CEH, CISSP, or equivalent.
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