Remote Azure DevOps (MLOps) Engineer - Lead

at NorthBay Solutions

Posted 3 weeks ago 1 applied

Description:

  • NorthBay is seeking a highly skilled Lead DevOps / MLOps Engineer (Azure, Terraform) to join their growing cloud and AI engineering team.
  • This role is ideal for candidates with a strong foundation in cloud DevOps practices and a passion for implementing MLOps solutions at scale.
  • Key responsibilities include designing, implementing, and managing CI/CD pipelines using tools such as Jenkins, GitHub Actions, or Azure DevOps.
  • The engineer will develop and maintain Infrastructure-as-Code using Terraform.
  • They will manage container orchestration environments using Kubernetes.
  • The role requires ensuring cloud infrastructure is optimized, secure, and monitored effectively.
  • Collaboration with data science teams to support ML model deployment and operationalization is essential.
  • The engineer will implement MLOps best practices, including model versioning, deployment strategies (e.g., blue-green), monitoring (data drift, concept drift), and experiment tracking (e.g., MLflow).
  • Building and maintaining automated ML pipelines to streamline model lifecycle management is also a key responsibility.

Requirements:

  • Candidates must have 3–7 years of experience in DevOps and/or MLOps roles.
  • Proficiency in CI/CD tools such as Jenkins, GitHub Actions, and Azure DevOps is required.
  • Strong expertise in Terraform and cloud-native infrastructure, preferably AWS, is necessary.
  • Hands-on experience with Kubernetes, Docker, and microservices is essential.
  • A solid understanding of cloud networking, security, and monitoring is required.
  • Scripting proficiency in Bash and Python is mandatory.

Benefits:

  • The position offers a full-time employment opportunity with a remote work option.
  • Employees will have the chance to work with a leading AWS Premier Partner in the cloud and AI engineering field.
  • The role provides an opportunity to implement MLOps solutions at scale, enhancing professional growth and expertise.
  • Collaboration with data science teams allows for a dynamic work environment and the chance to influence ML model deployment and operationalization.