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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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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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, 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
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integrity, efficiency, and scalability. Partner with researchers and vendors to design and deliver automated and cloud-based tools that improve research efficiency and usability. Ensure compliance with data
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management methodologies. Knowledge of cloud platforms (e.g., AWS, Azure) is a plus. Fresh graduates with a keen interest in AI-assisted development and project management are welcome to apply. Excellent
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who is passionate and willing to learn new languages and technologies. Experience in the Software Development Life Cycle (SDLC) framework and Agile project management methodologies. Knowledge of cloud
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such as R/Python/Java or similar languages, and familiarity with Unix-based platforms is advantageous Experience working in cloud environments (e.g., AWS) is beneficial High motivation and willingness
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programmes while balancing against the effort to implement, deploy, and maintain the suite of web applications on the cloud. Duties and Responsibilities The Full-Stack Developer is expected to: - Feature
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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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documentation for all related changes. Application Development: Develop new technical applications and maintain existing ones. Cloud Infrastructure Setup: Set up open-source cloud infrastructure using tools