MLOps Engineer

What you will do
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 you bring
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)
About us
ARRISE is a global leader in software development and services for the iGaming industry, bringing together more than 2,500 professionals operating across more than 12 locations worldwide.
From concept through to delivery, we combine technical expertise, creativity and industry knowledge to develop products, platforms and services that shape player experiences and support some of the industry's leading brands.
We are best known for the work we deliver for Pragmatic Play, creating and supporting technology, products and specialists services enjoyed by millions of players worldwide.
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