MLOps Engineer
ARRISE is looking for an MLOps Engineer to design and operate scalable inference and serving systems for ML workloads. The role involves designing and maintaining automated data, training, and inference pipelines.
The successful candidate will build and manage CI/CD pipelines for application testing, validation, and deployment. They will also monitor and maintain deployed APIs to ensure performance, reliability, and security. The position requires creating and managing internal platforms to configure and manage ML systems in production, and developing observability dashboards and alerting systems for model and infrastructure health. Implementation of unit and integration tests for ML code, pipelines, and deployment workflows, along with following security best practices in containerised deployments and data handling, are also key responsibilities.
- 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 containerised deployments and data handling.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (required).
- Proficient in Python with strong software architecture and development skills (required).
- Expertise in cloud platforms, preferably Azure, for architecting scalable and reliable ML systems (preferred).
- Strong knowledge of version control systems, package management, dependency tracking (required).
- Expertise in containerisation using Docker for scalable and maintainable system deployments (required).
- Experience with monitoring, logging, and alerting for ML systems and infrastructure (required).
- Knowledge of general Machine Learning concepts and algorithms (required).
- Proven experience deploying and managing ML models in production environments (required).
- Knowledge of data modelling, ETL processes, and database systems (SQL and NoSQL) (required).
Pragmatic Play is a leading content provider to the iGaming industry, delivering a multi-product portfolio of online slots, live casino, bingo and virtual sports to operators worldwide. Founded in 2015 and headquartered in Gibraltar, it releases new titles at a high cadence and is certified across numerous regulated markets. It operates as part of the ARRISE group, which runs the live-casino studios and operational hubs across Europe, the Middle East and Latin America that power its games.