Full Stack AI Engineer - Security

Remote position, only for professional based in Argentina or UruguayAt Ryz Labs we are looking for a Security AI Engineer to design, build, and deploy AI-driven systems that protect one of our team's platforms, users, and data. You’ll sit at the intersection of machine learning, cybersecurity, and engineering—developing intelligent defenses against threats such as fraud, abuse, intrusion, and data [link removed] role blends hands-on ML development with real-world security problem-solving and close collaboration with security, infrastructure, and product teams.

Essential Responsibilities:

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 [link removed] techniques such as anomaly detection, graph analysis, NLP, and behavioral modeling to security use [link removed] AI security solutions into production systems with high reliability and low [link removed] with Security, DevOps, and Platform teams to embed AI-driven protections into existing tools and [link removed] model performance, address drift, and continuously improve detection accuracy and [link removed] emerging threats and adversarial techniques, including adversarial ML, and proactively adapt [link removed] to incident response by providing AI-based insights and automation.Qualifications/Requirements of the Position:Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field; Master’s degree preferredStrong experience in machine learning or applied AI, with production deployment [link removed] 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.Knowledge, Skills, and Abilities Required:Background in cybersecurity, fraud detection, trust & safety, or abuse [link removed] with graph-based ML, NLP for security signals, or time-series anomaly [link removed] of adversarial ML, model evasion techniques, or secure model [link removed] building systems that operate under strict latency or reliability [link removed] work in regulated or high-risk [link removed] certifications or coursework (e.g., OSCP, CISSP concepts).Experience with SIEM/SOAR tools or security telemetry [link removed], talks, or open-source contributions in AI or security.

Originally posted on Himalayas