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dynamic workloads, while bridging theoretical research with practical implementation through close collaboration with cross-functional teams. Job Description: Conduct cutting-edge research in cloud
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research, education, and services in data science, AI, and biomedical computation. We invite applications for a Research Asisstant (Cloud Infrastructure Engineer) role in CBDS. The successful candidate will
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systems. Lead end-to-end delivery of cloud-native, high-availability applications from architecture to production, to guide engineering teams while aligning technology execution with business and research
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Description To design, build, and maintain hybrid scientific data platforms, focusing on backend systems that support both cloud-based and on-premise infrastructures. The role enables secure, human-centred
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that is clean, modular, and maintainable. MLOps & Cloud Proficiency: Fluent in the essential MLOps toolkit, including Git, Docker, and CI/CD principles. Have experience building data and model pipelines
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. Software Engineering Excellence: Demonstrates expert-level Python skills and a strong commitment to Software Engineering best practices, writing code that is clean, modular, and maintainable. MLOps & Cloud
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eye for detail Digital literacy in Microsoft Office and general cloud software These competencies are not compulsory, but any would be advantageous: Domain experience in education / graduate studies B2G
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on Rails. Strong knowledge of web application architecture, database management (SQL/NoSQL), and API development (RESTful or GraphQL). Strong knowledge of cloud platforms such as AWS, Google Cloud, Azure
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, and maintainable. MLOps & Cloud Proficiency: Fluent in the essential MLOps toolkit, including Git, Docker, and CI/CD principles. Have experience building data and model pipelines on at least one major
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, Kubernetes, Helm). Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform. A systematic approach to development and engineering, including debugging, DevOps/MLOps practices, and agile