[Hiring] MLOps Engineer @ARRISE

Role Description

We are seeking a highly skilled MLOps Engineer to join our global Data Science team and drive the deployment, scaling, and reliable operation of machine learning systems in production. You will help build large-scale, production-grade systems that impact millions of players worldwide, working closely with Data Scientists, Software Engineers, and Data Engineers, and own the full MLOps lifecycle.

If you thrive in a fast-growing, high-impact environment and enjoy designing and operating systems at scale, we encourage you to apply. Even if you don’t meet every requirement, your skills and ability to deliver impact are what matter most.

Qualifications

• 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).

Requirements

• 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.

Benefits

• Competitive compensation based on your experience and impact.
• Opportunities for professional and personal development.
• Work on state-of-the-art machine learning infrastructure and systems at scale.
• Opportunities to contribute to open-source projects and stay active in the ML community.
• Opportunity to make a measurable and visible impact within a large-scale organization.
• Flexible working hours and remote-friendly setup.