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4 - 9 years
20 - 25 Lacs
Patna
Work from Office
8 to 10 years of industry experience working in Software Engineering, DevOps or Data Engineering with Data Science and MLOps experience. Strong DevOps, Data Engineering and Client background with Azure Experience Experience in Distributed computing, Data pipelines, and AI/Client. Review and influence the engineering design, architecture and technology stack across multiple products Experience in Azure ML, Data Bricks, and Azure Kubernetes service. Extensive experience with Unix/AIX/Linux environments Experience with automation servers such as Jenkins, CloudBees, Travis, GitHub actions Experience with logging tools such as Splunk, Kibana, Logstash Familiarity with setting up model and experiment Versioning technologies like MLFLow/Kubeflow Expertise in design, implement and troubleshoot machine learning models using Azure Machine Learning platform Strong knowledge in Python and Kubernetes Develop and maintain MLOps pipelines using Azure ML and DevOps Experience with code scanning and application security. Roles & Responsibilities Ensure reliability and cost saving. Scale the proof of concept product to enterprise grade application with all the required components for reliability, scalability, monitoring and security. Suggest and implement the best practices from Software engineering to ML workflow to ensure CI/CD, reproducibility and quick delivery cycle. Lead and drive the deployment of ML models, life cycle management and monitoring of Machine Learning(ML) and Deep Learning (DL) models in in all stages leading to production Be a subject matter expert on DevOps practices, CI/CD and Configuration Management with assigned engineering team Automate and streamline ML operations and processes.
Posted 2 months ago
4 - 9 years
20 - 25 Lacs
Bengaluru
Work from Office
8 to 10 years of industry experience working in Software Engineering, DevOps or Data Engineering with Data Science and MLOps experience. Strong DevOps, Data Engineering and Client background with Azure Experience Experience in Distributed computing, Data pipelines, and AI/Client. Review and influence the engineering design, architecture and technology stack across multiple products Experience in Azure ML, Data Bricks, and Azure Kubernetes service. Extensive experience with Unix/AIX/Linux environments Experience with automation servers such as Jenkins, CloudBees, Travis, GitHub actions Experience with logging tools such as Splunk, Kibana, Logstash Familiarity with setting up model and experiment Versioning technologies like MLFLow/Kubeflow Expertise in design, implement and troubleshoot machine learning models using Azure Machine Learning platform Strong knowledge in Python and Kubernetes Develop and maintain MLOps pipelines using Azure ML and DevOps Experience with code scanning and application security. Roles & Responsibilities Ensure reliability and cost saving. Scale the proof of concept product to enterprise grade application with all the required components for reliability, scalability, monitoring and security. Suggest and implement the best practices from Software engineering to ML workflow to ensure CI/CD, reproducibility and quick delivery cycle. Lead and drive the deployment of ML models, life cycle management and monitoring of Machine Learning(ML) and Deep Learning (DL) models in in all stages leading to production Be a subject matter expert on DevOps practices, CI/CD and Configuration Management with assigned engineering team Automate and streamline ML operations and processes.
Posted 2 months ago
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