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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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. 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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, 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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, writing code 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
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Engineering best practices, writing code that is clean, modular, and maintainable. MLOps & Cloud Proficiency: Fluent in the essential MLOps toolkit, including Git, Docker, and CI/CD principles. Have experience
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with container and container orchestration technologies (e.g., Docker, Kubernetes, Helm). Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform. A systematic approach to
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of our intelligent operations. As an AI & Systems Engineer, you’ll design and scale the systems that power our AI agents and data-driven workflows — from API integration to cloud architecture and data lake
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learning, including unsupervised and camera-aware person re-identification, with the ability to design, implement, and evaluate robust vision models. Experience in integrating research prototypes into cloud
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software development tools and platforms (e.g., IDEs, cloud environments etc). Demonstrated ability to coordinate projects and manage multiple tasks in a structured and efficient manner. Effective