Your day-to-day impact:

• Own the end-to-end design, development, evaluation, and deployment of Machine Learning and AI solutions, ensuring scalability, reliability, and performance;
• Develop and optimize Machine Learning models, with a strong focus on classical Machine Learning techniques such as XGBoost, Random Forest, and Decision Trees, ensuring robustness, explainability, and efficiency;
• Build models and strategies focused on fraud prevention, chargeback risk, and risk prevention in credit card payments;
• Translate business and payment-related challenges into effective Machine Learning solutions, considering both technical performance and business impact;
• Build and maintain production-grade solutions for AI applications, including APIs, data pipelines, and services, in collaboration with AI Engineering and MLOps specialists;
• Implement and oversee continuous integration and continuous delivery (CI/CD) pipelines for AI model deployment and updates;
• Collaborate closely with data scientists, AI engineers, MLOps specialists, software engineers, and stakeholders across teams to translate business requirements into actionable AI solutions;
• Troubleshoot production issues related to AI and Machine Learning systems, performing rigorous testing, validation, and continuous improvement;
• Contribute to the evolution of AI-powered solutions and explore new ways of applying AI to improve development efficiency and business outcomes;
• Mentor team members on best practices in Machine Learning modeling, AI engineering, model deployment, and system integration.

• Hands-on experience in Data Science and Machine Learning modeling;
• Proven expertise in developing and evaluating Machine Learning models, especially using XGBoost, Random Forest, and Decision Trees;
• Strong programming skills in Python, with demonstrated experience in developing high-quality, maintainable code;
• Experience working with Machine Learning models in real-world or production environments;
• Strong analytical and problem-solving skills, with the ability to translate business challenges into Machine Learning solutions;
• Proficiency with cloud platforms (AWS, GCP or Azure), including deploying AI services and managing cloud resources;
• Strong knowledge of CI/CD tools and processes (GitHub Actions);
• Solid understanding of data engineering concepts, ETL processes, and data pipeline architectures;
• Experience with monitoring and logging solutions for ML systems (e.g., CloudWatch or DataDog);
• Awareness of security standards and compliance requirements relevant to AI and data-driven systems;
• Conversational English proficiency, with the ability to communicate effectively with international stakeholders.

• Prior experience in fraud prevention, fraud detection, chargeback, payment risk, financial services, fintech, or related industries;
• Experience developing or working with neural networks and other Machine Learning techniques;
• Experience with containerization tools (Docker);
• Experience collaborating closely with MLOps and AI Engineering teams;
• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field;