Full Stack AI Engineer - Security

Remote position, only for professional based in Argentina or Uruguay

At 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 leakage.

This role blends hands-on ML development with real-world security problem-solving and close collaboration with security, infrastructure, and product teams.

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.

Qualifications/Requirements of the Position:

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.

Knowledge, Skills, and Abilities Required:

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.