ML Engineer Advanced Analytics (MLOps)

As an ML Engineer specializing in Advanced Analytics, your role will involve leveraging your expertise in MLOps and Azure AI to contribute to the development and deployment of machine learning solutions in a cloud-native environment. You will be responsible for building, deploying, monitoring, and managing AI/ML solutions on Microsoft Azure. Key Responsibilities: - Utilize Azure Machine Learning, Azure OpenAI Service, Azure Cognitive Services, and Azure Speech Services to develop and deploy machine learning models - Proficient in Python Programming for data analysis and model development - Implement CI/CD Pipelines for automated deployment processes - Experience with Docker and Kubernetes / AKS for containerization and orchestration - Familiarity with Azure DevOps for efficient project management - Use Azure CLI for command-line interface tasks - Knowledge of Azure SDKs for integrating Azure services into applications - Monitor and analyze application performance using Azure Monitor & Application Insights - Implement Managed Identity & RBAC for secure access control - Manage the MLOps Lifecycle for seamless deployment and maintenance of ML solutions Qualifications Required: - 7-10 years of experience in machine learning engineering with a focus on MLOps and Azure AI - Strong proficiency in Azure services including Azure Machine Learning, Azure Cognitive Services, and Azure OpenAI Service - Proficient in Python programming for data manipulation and model development - Experience with Docker, Kubernetes / AKS, and CI/CD Pipelines for deployment automation - Familiarity with Azure DevOps, Azure CLI, and Azure SDKs for effective project management and integration - Knowledge of Managed Identity & RBAC for secure access control - Ability to monitor and analyze application performance using Azure Monitor & Application Insights Please note that the above job posting has been aggregated from an external source, and details are subject to change. Kindly verify the latest information directly on the company's website before applying. As an ML Engineer specializing in Advanced Analytics, your role will involve leveraging your expertise in MLOps and Azure AI to contribute to the development and deployment of machine learning solutions in a cloud-native environment. You will be responsible for building, deploying, monitoring, and managing AI/ML solutions on Microsoft Azure. Key Responsibilities: - Utilize Azure Machine Learning, Azure OpenAI Service, Azure Cognitive Services, and Azure Speech Services to develop and deploy machine learning models - Proficient in Python Programming for data analysis and model development - Implement CI/CD Pipelines for automated deployment processes - Experience with Docker and Kubernetes / AKS for containerization and orchestration - Familiarity with Azure DevOps for efficient project management - Use Azure CLI for command-line interface tasks - Knowledge of Azure SDKs for integrating Azure services into applications - Monitor and analyze application performance using Azure Monitor & Application Insights - Implement Managed Identity & RBAC for secure access control - Manage the MLOps Lifecycle for seamless deployment and maintenance of ML solutions Qualifications Required: - 7-10 years of experience in machine learning engineering with a focus on MLOps and Azure AI - Strong proficiency in Azure services including Azure Machine Learning, Azure Cognitive Services, and Azure OpenAI Service - Proficient in Python programming for data manipulation and model development - Experience with Docker, Kubernetes / AKS, and CI/CD Pipelines for deployment automation - Familiarity with Azure DevOps, Azure CLI, and Azure SDKs for effective project management and integration - Knowledge of Managed Identity & RBAC for secure access control - Ability to monitor and analyze application performance using Azure Monitor & Application Insights Please note that the above job posting has been aggregated from an external source, and details are subject to change. Kindly verify the latest information directly on the company's website before applying.