Job Responsibilities: 

10+ years of experience required to apply senior resources needed

                   Research and implement MLOps tools, frameworks and platforms for our Data Science projects. 

                   Work on a backlog of activities to raise MLOps maturity in the organization. 

                   Proactively introduce a modern, agile and automated approach to Data Science. 

                   Conduct internal training and presentations about MLOps tools’ benefits and usage. 

                   Management and development of continuous integration and deployment 

                   Prototyping and developing cloud-native architecture solutions for application needs, particularly with AWS 

                   Providing infrastructure-as-code utilizing Terraform and AWS Cloud Formation 

Requirements: 

                   Wide experience with Kubernetes and Docker is a must have 

                   Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, h2O etc). 

                   Good understanding of ML and AI concepts. Hands-on experience in ML model development 

                   Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit. 

                   Experience in CI/CD/CT pipelines implementation. 

                   Experience with AWS cloud platforms is a must have. 

                   Providing infrastructure-as-code utilizing Terraform and AWS Cloud Formation 

                   Experience with Data Science IDE tools like Jupyter Notebook and RStudio

Thanks & Regards,

Senthil 

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