Full Stack AI Engineer – Security

🕒 February 19

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Ryz Labs

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11 - 50 employees

Ryz Labs builds startups from the ground-up and helps other startups scale by providing top-tier technical talent solutions.

📋 Description

• Design and implement AI/ML models to detect, prevent, and respond to security threats (e.g., fraud, abuse, anomalies, malware, insider risk). • Build and maintain pipelines for data ingestion, feature engineering, model training, evaluation, and deployment. • Apply techniques such as anomaly detection, graph analysis, NLP, and behavioral modeling to security use cases. • Integrate AI security solutions into production systems with high reliability and low latency. • Partner with Security, DevOps, and Platform teams to embed AI-driven protections into existing tools and workflows. • Monitor model performance, address drift, and continuously improve detection accuracy and resilience. • Research emerging threats and adversarial techniques, including adversarial ML, and proactively adapt defenses. • Contribute to incident response by providing AI-based insights and automation.

🎯 Requirements

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field; Master’s degree preferred • Strong experience in machine learning or applied AI, with production deployment experience. • Solid foundation in security concepts (e.g., threat modeling, authentication, authorization, network or application security). • Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn). • Experience working with large-scale data systems (SQL/NoSQL, streaming pipelines, logs, telemetry). • Familiarity with cloud platforms and MLOps practices (CI/CD, monitoring, model lifecycle management). • Ability to reason about trade-offs between security, performance, and usability. • Background in cybersecurity, fraud detection, trust & safety, or abuse prevention. • Experience with graph-based ML, NLP for security signals, or time-series anomaly detection. • Knowledge of adversarial ML, model evasion techniques, or secure model design. • Experience building systems that operate under strict latency or reliability constraints. • Prior work in regulated or high-risk environments. • Security certifications or coursework (e.g., OSCP, CISSP concepts). • Experience with SIEM/SOAR tools or security telemetry platforms. • Publications, talks, or open-source contributions in AI or security.