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Strong employer brand, mid-level 3+yrs, metro locations, and popular ML-security skills increase candidate density.
Combined ML and cybersecurity focus requires domain-specific experience, limiting cross-industry transferability.
Explicit 3+ years plus mandatory ML production, cloud, MLOps, and security tool experience tightens screening.
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Design and implement scalable AI infrastructure for cybersecurity applications including model training, deployment, and monitoring.
Build and optimize AI agents for threat detection, triage, and automated response integrated with existing security tools like SIEM, SOAR, and EDR.
Lead development of data pipelines and feature engineering workflows, ensure reliability and security of AI systems in production, and mentor junior engineers.
Bachelor’s or Master’s degree in Computer Science, AI, or related field.
Minimum 3 years of experience building and deploying machine learning or AI systems in production.
Strong programming skills in Python; experience with ML frameworks such as TensorFlow or PyTorch.
Experience with cloud-based AI infrastructure (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes).
Experienced in integrating AI solutions with cybersecurity tools and workflows (SIEM, SOAR, EDR platforms).
Knowledgeable in cybersecurity principles, including network security controls (IDS/IPS) and incident response automation.
Skilled in MLOps, CI/CD practices, and working with real-time data processing and streaming frameworks.