Full Stack AI Engineer - Security
RemoteMexico
Job Summary
Design and scale AI/ML models to detect, prevent, and respond to security threats like identity risk, fraud, and malware using anomaly detection, graph analysis, and NLP. Build and maintain robust data ingestion pipelines, real-time streaming systems, and high-performance microservices to process high volumes of security events. Deploy AI-driven defenses directly into production workflows by integrating secure model design and automated incident response to reduce manual investigation cycles. Partner with DevSecOps teams to embed protections into containerized environments. This role requires a background in cybersecurity and fraud detection, proficiency in Python and distributed systems, and must be based in Mexico.
Required Qualifications
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field
- 5+ years of software engineering experience, with a heavy focus on backend architecture, API design, and distributed systems
- 3+ years of experience in cybersecurity, fraud detection, trust & safety, or abuse prevention
- 2+ years of hands-on experience building and deploying machine learning models in a production environment
- High proficiency in English with strong collaboration and communication skills
- Must be based in Mexico and available to work remotely
Desired Qualifications
- Master's degree
- Experience with distributed systems, stream processing, and containerized deployments using AWS, Docker, and Kubernetes
- Strong background in cybersecurity, fraud detection, trust & safety, or threat intelligence, alongside a deep understanding of secure system design and OWASP principles
- Proven experience designing robust, scalable microservices capable of handling strict latency and reliability constraints
- Ability to effectively balance the trade-offs between rigorous security, high system performance, and a positive user experience
- Enjoy staying up-to-date with secure AI frameworks (such as ISO/IEC 42001 or OWASP) and exploring advanced methods like time-series anomaly detection and graph analysis
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