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Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
Capgemini was founded by Serge Kampf in 1967 as an enterprise management and data processing company. The company was founded as the Société pour la Gestion de l'Entreprise et le Traitement de l'Information (Sogeti).In 1974 Sogeti acquired Gemini Computers Systems, a US company based in New York.In 1975, having made two major acquisitions of CAP (Centre d'Analyse et de Programmation) and Gemini Computer Systems, and following resolution of a dispute with the similarly named CAP UK over the international use of the name 'CAP', Sogeti renamed itself as CAP Gemini Sogeti.
Job ID: 149012765
Skills:
XGBoost, Keras, Python, Model Deployment and Productionlization, Model Engineering and Improvement, scikit-learn, Machine Learning Model Development, data augmentation, Data Handling SQL, feature engineering, feature selection
Skills:
Unix, Gcp, MLops, Linux, Pyspark, Spark, Azure, Python, AWS, CI CD pipeline, Model deployment
Skills:
Agile Development, Tensorflow, Pytorch, MLops, Python, Machine Learning Algorithms, generative AI applications, software engineering best practices, Data Processing, model training, generative AI concepts, Agentic AI, feature engineering, responsible AI practices
Skills:
data engineering , S3, Big Data Technologies, MLops, Docker, Cloud Services, Kubernetes, Python, Etl, Infrastructure as Code, EKS, Containerized application development, Glue, Athena
Skills:
data engineering , snowflake , Ml, Azure Data Factory, Devops, Pyspark, Power Bi, Azure Databricks, Automation, Python, Azure, Denodo, Azure DevOps, YAML pipelines, Azure Cognitive Services, Azure Key Vault, Snowpark, Ai, ADLSv2, Azure App Services
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