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  "job_title": "Junior GenAI Platform Architect",
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  "employer_name": "Capgemini Deutschland Holding GmbH",
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  "job_posted_at": "vor 8 Tagen",
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  "job_description": "Your role in the team\n• As a (Junior) AI Platform Architect, you support the development of modern AI platforms in cloud and on-premises environments.\n• You work closely with experienced architects and learn how to handle MLOps tools and AI workflows.\n• You assist with the implementation of AI pipelines, model serving components, and automation processes.\n• You are involved in the integration of AI services from hyperscalers (AWS, Azure, Google) as well as sovereign clouds.\n• You conduct initial analyses, troubleshooting tasks, and optimizations.\n• You assist with Monitoring & Observability for AI workloads.\n• You document technical results and participate in quality assurance of AI deployments.\n\nWhat we offer\n• We place great importance on work-life balance.\n• Therefore, you have the opportunity to work hybrid from the office, from home, or at other Capgemini locations in Germany.\n• At the same time, Capgemini offers you flexible working time models.\n• We offer you a variety of opportunities for further development.\n• Our career model allows you to steer your career in the way that suits you best, and our leadership programs support you in doing so.\n• With numerous offerings on our learning platform, you also have the opportunity at any time to expand your skills or acquire new ones.\n• Our communities: We offer active employee networks where you can exchange ideas both professionally and personally with colleagues from around the world.\n• Additionally, our communities promote diversity, inclusion, and equity in the workplace.\n\nTechnologies and skills\n• Kubeflow\n• Azure\n• Kubernetes\n• Google Cloud Platform\n• Amazon SageMaker\n• Docker\n• MLflow\n• AWS\n• StackIT\n\nOur expectations:\n\nQualifications\n• Basic knowledge of cloud platforms (AWS, Azure, Google, STACKIT) and container technologies (Docker, Kubernetes).\n• Interest in model serving, vector databases, and LLM-based AI.\n• Basic understanding of data pipelines, streaming, or data lakes.\n• Ability to work in a team, willingness to learn, and a structured way of working.\n• Good knowledge of German and English.\n\nExperience\n• Initial experience with MLOps frameworks such as MLflow, Kubeflow, or SageMaker is a plus.\n\nEducation\n• Completed studies or vocational training in STEM fields and a strong motivation for AI and platform technologies.\n\nBenefits\n• Fitness Offers\n• Health Care Benefits\n• Company Car\n• * Flexible Working Hours\n• Jobbike",
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  "job_posted_at_datetime_utc": "2026-03-13T00:00:00.000Z"
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