Behaviorally
Azure Dev Ops / ML Ops engineer
Behaviorally, New York, New York, us, 10261
Azure Dev Ops / ML Ops Engineer
MUST:
Hybrid in New York City, NY.
Full Time PositionNo Sponsorship ProvidedMinimum Qualifications:The role is ideal for someone who thrives in a fast-paced, cutting-edge technology environment and implementing best-in-class DevOps / MLOps practices.5+ years of hands-on experience in DevOps in Microsoft Azure Cloud with a focus on MLOPs:Networking: Azure Load balancer, Azure application gatewayCompute: Azure FunctionsMonitoring: Azure MonitorContainer orchestration: KubernetesIaaC: ARM templatesML Ops: Azure ML, ML FlowExperience with implementing integration solutions with Microservices, RESTful Web Services, and Web APIs.Solid knowledge of CI/CD pipelines and experience with tools like Jenkins, Git, and Docker.Strong understanding of computer vision techniques, including CNN, object detection, and image segmentation.Proven experience in developing and deploying machine learning models, with a focus on computer vision applications.Proficient knowledge of SQL with any RDBMS and PowerBI.Experience working and communicating cross-functionally in a team environment.Live within commuting distance to one of Behaviorally's offices.Preferred Qualifications:Certifications in AI/ML technologies and Azure, such as Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect.Responsibilities:Collaborate with product teams to design scalable and efficient solutions, ensuring alignment with architectural best practices and business requirements.Assist in the development and optimization of machine learning algorithms and models, providing guidance on best practices and methodologies.Support the design and implementation of data pipelines for data ingestion, processing, and feature engineering, ensuring data quality and integrity.Build and Maintain CI/CD Pipelines: Design, implement, and manage robust Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning models and applications. Collaborate with cross-functional teams to establish and enhance CI/CD best practices.Utilize your expertise in Azure to architect and deploy machine learning solutions within the Azure ecosystem. Manage and optimize Azure-based infrastructure, ensuring security, scalability, reliability, and performance.Implement and manage deployment strategies for machine learning models in development and production environments. Collaborate with data scientists and engineers to streamline the deployment process and monitor model performance.Create comprehensive testing protocols for machine learning models, ensuring thorough evaluation and validation in different environments. Implement automated testing procedures to guarantee the reliability and accuracy of deployed models. Develop and implement monitoring solutions to ensure the health and performance of deployed machine learning models. Proactively identify and address issues related to scalability, efficiency, and reliability.Collaboration and Documentation: Work closely with data scientists, software engineers, and other stakeholders to understand requirements and integrate machine learning models into the overall system. Create and maintain comprehensive documentation for CI/CD pipelines, deployment processes, and infrastructure configurations.
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MUST:
Hybrid in New York City, NY.
Full Time PositionNo Sponsorship ProvidedMinimum Qualifications:The role is ideal for someone who thrives in a fast-paced, cutting-edge technology environment and implementing best-in-class DevOps / MLOps practices.5+ years of hands-on experience in DevOps in Microsoft Azure Cloud with a focus on MLOPs:Networking: Azure Load balancer, Azure application gatewayCompute: Azure FunctionsMonitoring: Azure MonitorContainer orchestration: KubernetesIaaC: ARM templatesML Ops: Azure ML, ML FlowExperience with implementing integration solutions with Microservices, RESTful Web Services, and Web APIs.Solid knowledge of CI/CD pipelines and experience with tools like Jenkins, Git, and Docker.Strong understanding of computer vision techniques, including CNN, object detection, and image segmentation.Proven experience in developing and deploying machine learning models, with a focus on computer vision applications.Proficient knowledge of SQL with any RDBMS and PowerBI.Experience working and communicating cross-functionally in a team environment.Live within commuting distance to one of Behaviorally's offices.Preferred Qualifications:Certifications in AI/ML technologies and Azure, such as Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect.Responsibilities:Collaborate with product teams to design scalable and efficient solutions, ensuring alignment with architectural best practices and business requirements.Assist in the development and optimization of machine learning algorithms and models, providing guidance on best practices and methodologies.Support the design and implementation of data pipelines for data ingestion, processing, and feature engineering, ensuring data quality and integrity.Build and Maintain CI/CD Pipelines: Design, implement, and manage robust Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning models and applications. Collaborate with cross-functional teams to establish and enhance CI/CD best practices.Utilize your expertise in Azure to architect and deploy machine learning solutions within the Azure ecosystem. Manage and optimize Azure-based infrastructure, ensuring security, scalability, reliability, and performance.Implement and manage deployment strategies for machine learning models in development and production environments. Collaborate with data scientists and engineers to streamline the deployment process and monitor model performance.Create comprehensive testing protocols for machine learning models, ensuring thorough evaluation and validation in different environments. Implement automated testing procedures to guarantee the reliability and accuracy of deployed models. Develop and implement monitoring solutions to ensure the health and performance of deployed machine learning models. Proactively identify and address issues related to scalability, efficiency, and reliability.Collaboration and Documentation: Work closely with data scientists, software engineers, and other stakeholders to understand requirements and integrate machine learning models into the overall system. Create and maintain comprehensive documentation for CI/CD pipelines, deployment processes, and infrastructure configurations.
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