Role Description At arrivia, we specialize in making brands better through the power of travel, combining decades of experience with a constant drive for technology innovation. This specific role is a fully remote opportunity based within Mexico. This role sits at the intersection of machine learning, cybersecurity, and full-stack engineering. You won't just be monitoring dashboards; you’ll be architecting intelligent defenses that keep our members and data safe from evolving digital threats by building high-scale, real-time security systems. As a core member of our IT Operations team, reporting to the Security Ops Lead, you will: - Design & Architect: Implement and scale AI/ML models to proactively detect, prevent, and respond to security threats such as identity risk, fraud, malware, and insider anomalies. - System Reliability: Build and maintain robust data ingestion, feature engineering, and real-time streaming pipelines (e.g., utilizing SQL/NoSQL databases and messaging frameworks like Kafka) to process high volumes of security events. - Deploy Microservices: Integrate AI security models directly into production workflows by developing high-performance, low-latency APIs and microservices (using frameworks like FastAPI, Flask, or Django). - Adversarial Research: Analyze adversarial attacks, threat modeling, and secure model design to adapt our defenses under evolving threat scenarios. - Automated Response: Enhance automated incident response, accelerating remediation workflows to significantly reduce manual threat investigation cycles. - Collaborative Impact: Partner with DevSecOps and Platform teams to build MLOps pipelines and embed AI-driven protections directly into containerized environments (Docker/Kubernetes). Qualifications - Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field (Master's preferred). - 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. Requirements - Skilled in Modern Tech: Solid foundation in Python, SQL, and ML frameworks like PyTorch, TensorFlow, or scikit-learn. - Experienced with Distributed Systems: Comfortable working with large-scale data systems, stream processing, and containerized deployments using AWS, Docker, and Kubernetes. - Security Focused: Strong background in cybersecurity, fraud detection, trust & safety, or threat intelligence, alongside a deep understanding of secure system design and OWASP principles. - API & Backend Specialist: Proven experience designing robust, scalable microservices capable of handling strict latency and reliability constraints. - Analytical Problem-Solver: Ability to effectively balance the trade-offs between rigorous security, high system performance, and a positive user experience. - Growth Mindset: 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. Benefits - Collaborative, inclusive environment where your work directly impacts how millions of people experience the world. - Opportunity to apply your AI and security expertise to a global mission from the comfort of your home office in Mexico.

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration. • Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP. • Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift. • Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts. • Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output. • Review AI-generated code before it ships for correctness, security, and type safety. • Operate what you ship: monitoring, reliability, cost, and failure modes in production. • Contribute to shared engineering standards, playbooks, and internal tooling.

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration. • Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP. • Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift. • Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts. • Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output. • Review AI-generated code before it ships for correctness, security, and type safety. • Operate what you ship: monitoring, reliability, cost, and failure modes in production. • Contribute to shared engineering standards, playbooks, and internal tooling.

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration. • Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP. • Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift. • Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts. • Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output. • Review AI-generated code before it ships for correctness, security, and type safety. • Operate what you ship: monitoring, reliability, cost, and failure modes in production. • Contribute to shared engineering standards, playbooks, and internal tooling.