MLOps Engineer

🕒 August 26

🇵🇹 Portugal – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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ARRISE

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🎮 Gaming

🤝 B2B

Gaming • B2B

ARRISE is a global software developer and services company focused on the iGaming industry. With more than 11,000 professionals across 14 locations, they design and build casino products including award-winning slot games, immersive live-casino experiences, and high-performance platform solutions that scale from concept through execution. ARRISE combines creative game design and technical engineering to drive player engagement and support operators and partners in the online gambling sector.

📋 Description

• 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

🎯 Requirements

• 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, and 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)

🏖️ Benefits

• Growth opportunities at all levels • Investment in employees • Strategic partnerships opening new opportunities for success