Full Stack AI Engineer - Security
New
Argentina or UruguayFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLPyTorchNosqlTensorflowCI/CDscikit-learnNLPMLOps
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
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
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