Senior DevOps Engineer

2 weeks ago


Saskatoon, Saskatchewan, Canada International Road Dynamics Inc Full time
Job Title: DevOps Engineer

The DevOps Engineer will play a pivotal role in enhancing the development and delivery infrastructure at International Road Dynamics Inc. with a focus on supporting both traditional software development and machine learning workflows.

This includes building, deploying, and maintaining CI/CD pipelines, with a particular emphasis on integrating and supporting machine learning operations (MLOps) for scalable and secure ML pipelines. The role will also focus on ensuring model development adheres to best practices like versioning, auditability, and data security.


Key Responsibilities
  • Design, implement, and manage robust CI/CD pipelines to facilitate seamless code integration and deployment, and machine learning (ML) model releases.
  • Develop and maintain automated solutions for infrastructure provisioning and management, and machine learning pipelines, ensuring consistency and scalability, and model versioning.
  • Monitor system performance, identify and resolve issues promptly, and ensure the availability and reliability of development and production environments for both Software and ML model environments.
  • Implement and manage security practices within the CI/CD pipelines and infrastructure to protect against vulnerabilities and ensure compliance with industry standards.
  • Continuously improve the performance of the infrastructure and CI/CD processes, and ML pipelines by identifying bottlenecks and implementing solutions for optimization.
Requirements
  • Bachelor's degree in Computer Science, Computer Engineering, or a related field.
  • 1-3 years of experience in a DevOps role or similar position.
  • Proficiency with CI/CD tools (e.g., Jenkins, Bitbucket, CircleCI) and infrastructure as code (e.g., Terraform, Ansible).
  • Experience with MLOps and developing ML pipelines, including model training, validation, and deployment.
  • Experience developing and maintaining machine learning systems built with open-source tools (e.g., TensorFlow, PyTorch, MLflow).


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