1 job opening found.
Forschungszentrum Jülich GmbH·Jülich
In this position, you will develop and evaluate innovative AI tools for integration into complex HPC workflows, collaborate closely with use case providers and researchers, and manage data resources for Exascale projects.
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BMW AG·Munich
In this role, you will develop scalable HPC and GPU infrastructures for training and optimizing modern AI models, efficiently integrating new models into production environments.
Agile Robots Ag·Munich
In this role, you build the infrastructure for distributed training, deployment, and experimentation, utilizing technologies like Kubernetes and PyTorch to efficiently transition ML models into production.
BWI GmbH·Berlin, Munich
In this role, you will plan, build, and operate scalable Kubernetes clusters with GPU support for AI services, optimize resources, and develop security policies in collaboration with the team.
Bundesdruckerei GmbH·Berlin
In this role, you will enhance our MLOps platform, implement monitoring solutions, operate CI/CD pipelines, and optimize infrastructure for scalability and cost efficiency while collaborating closely with the team.
In this role, you will plan and operate high-performance Kubernetes clusters for AI applications, optimize resources, implement security policies, and develop monitoring solutions in an agile team.
In this role, you will enhance the MLOps platform, monitor models, optimize infrastructure, and maintain CI/CD pipelines to automate the lifecycle of machine learning models.
In this role, you will enhance the MLOps platform, implement monitoring solutions, optimize CI/CD pipelines and collaborate closely with data scientists to efficiently deploy and monitor AI models.
Further development and operation of our MLOps platform, including cloud resources and local servers. Implementation of monitoring solutions to oversee the performance and status of the deployed models. Optimization of ML infrastructure with regard to...
Further development and operation of our MLOps platform, including cloud resources and local servers. Implementation of monitoring solutions to monitor the performance and status of the deployed models. Optimizing the ML infrastructure for scalability...