Full Stack AI Engineer - Security

RYZ Labs; Buenos Aires; Presencial; ARS 1.674.000 - 2.232.000; Jornada completa

Descripción de la vacante

A technology company is seeking a Security AI Engineer to develop AI-driven systems that protect platforms from security threats. This remote position is open to professionals in Argentina or Uruguay. Responsibilities include designing models to detect fraud, building data pipelines, and integrating AI solutions into production systems. Candidates should have a relevant degree, experience in machine learning, strong security knowledge, and proficiency in Python and ML frameworks. Knowledge of adversarial ML and security certifications are pluses.

Formación

• Bachelor's degree in a relevant field; Master's preferred.
• Strong experience in machine learning with production deployment.
• Solid foundation in security concepts like threat modeling.

Responsabilidades

• Design and implement AI/ML models for security threat detection.
• Build and maintain data processing pipelines for model training.
• Monitor model performance and continuously improve detection accuracy.

Conocimientos

Machine learning

Cybersecurity concepts

Python proficiency

Experience with ML frameworks

Data systems experience

Educación

Bachelor’s degree in Computer Science or related field

Master’s degree preferred

Herramientas

PyTorch

TensorFlow

SQL

NoSQL

Descripción del empleo

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.

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 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.