MLOps Engineer

Job description

ABOUT US: ARRISE sets the benchmark for service delivery and excellence in the iGaming industry. Playing a key role in the success of its clients, which include Pragmatic Play, a brand relied upon by the world’s biggest online casinos for its cutting-edge products, ARRISE helps to deliver exceptional gaming experiences to millions of players worldwide. Our global team of over 12,000 talented and driven professionals are shaping the future of iGaming. Headquartered in Gibraltar, we have offices spanning Canada, India, the Isle of Man, Latvia, Malta, Romania, Serbia, Bulgaria, and the UAE, and more exciting destinations on the horizon. At ARRISE, we take pride in creating growth opportunities at all levels, constantly investing in our people while welcoming new colleagues and forging strategic partnerships that open new opportunities for success. To achieve this, we bet on ourselves. We know that success is a collective effort, and our team is driven by ambition, collaboration, and a shared commitment to grow and succeed — while embracing every step of the journey. Be part of the future of iGaming with 12,000 ARRISERS! See a job that excites you? Apply now, and our friendly recruitment team will connect with you soon. Your journey starts here. WHAT YOU’ll BE DOING Design and operate scalable inference and serving systems for ML workloads. Design and maintain automated data, training, and inference pipelines. Build and manage CI/CD pipelines for application testing, validation, and deployment Monitor and maintain deployed APIs to ensure performance, reliability, and security. Create and manage internal platforms to configure and manage ML systems in production. Develop observability dashboards and alerting systems for model and infrastructure health. Implement unit and integration tests for ML code, pipelines, and deployment workflows. Follow security best practices in containerized deployments and data handling. WHAT WE ASK OF YOU Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proficient in Python with strong software architecture and development skills. Expertise in cloud platforms, preferably Azure, for architecting scalable and reliable ML systems. Strong knowledge of version control systems, package management, dependency tracking. Expertise in containerization using Docker for scalable and maintainable system deployments. Experience with monitoring, logging, and alerting for ML systems and infrastructure. Knowledge of general Machine Learning concepts and algorithms. Proven experience deploying and managing ML models in production environments. Knowledge of data modelling, ETL processes, and database systems (SQL and NoSQL)