Azure MLOps Engineer

2 weeks ago

Teaneck, New Jersey, United States BEHAVIORALLY INC Full time
Job Overview

This position is tailored for individuals who excel in dynamic, innovative technology settings and are adept at implementing top-tier DevOps and MLOps methodologies.

Essential Qualifications:

  • Over 5 years of practical experience in DevOps within the Microsoft Azure Cloud, specifically focusing on MLOps, which includes:
    • Networking: Proficiency with Azure Load Balancer and Azure Application Gateway.
    • Compute: Experience with Azure Functions.
    • Monitoring: Familiarity with Azure Monitor.
    • Container Orchestration: Knowledge of Kubernetes.
    • IaaC: Experience with ARM templates.
    • MLOps: Proficient in Azure ML and ML Flow.
  • Experience in developing integration solutions utilizing Microservices, RESTful Web Services, and Web APIs.
  • Strong understanding of CI/CD pipelines and familiarity with tools such as Jenkins, Git, and Docker.
  • Solid grasp of computer vision techniques, including Convolutional Neural Networks (CNN), object detection, and image segmentation.
  • Demonstrated experience in creating and deploying machine learning models, particularly in computer vision applications.
  • Proficient in SQL and experienced with any RDBMS and PowerBI.
  • Adept at collaborating and communicating effectively in a team-oriented environment.
  • Must reside within a reasonable 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.

Key Responsibilities:

  • Collaborate with product teams to design scalable and efficient solutions, ensuring they align with architectural best practices and business needs.
  • Assist in the development and enhancement of machine learning algorithms and models, offering 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. Work with cross-functional teams to establish and improve CI/CD best practices.
  • Utilize 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 both 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 across 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 identifying and addressing 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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