Senior Machine Learning Operations Architect

4 days ago


Detroit, Michigan, United States Canopy Full time
Transforming Vehicle Security with AI-Powered Innovation

At Canopy, we're revolutionizing vehicle security by building cutting-edge technology. As a Staff MLOps Engineer, you'll play a critical role in leading the design, development, and implementation of scalable machine learning operations (MLOps) pipelines and infrastructure.

About the Role

This is a high-impact opportunity to shape the future of MLOps at Canopy. You'll work closely with cross-functional teams to streamline and automate data ingestion, model deployment, ensuring high availability, reliability, and performance of AI-driven solutions.

Key Responsibilities
  • MLOps Pipeline Development: Design, implement, and maintain robust MLOps services and pipelines to automate the deployment and monitoring of machine learning models in production.
  • Collaboration and Integration: Work with engineers to integrate machine learning models into existing systems and workflows, ensuring seamless integration with cloud or on-premises infrastructure.
  • CI/CD Best Practices: Develop and enforce best practices for continuous integration and continuous delivery (CI/CD) in machine learning projects, including version control, automated testing, and continuous integration.
  • Model Performance Monitoring: Monitor the performance and health of data and models in production, implementing strategies for retraining, versioning, and rollback when necessary.
  • Infrastructure Optimization: Optimize infrastructure for model training and inference, ensuring scalability, efficiency, and cost-effectiveness.
Requirements
  • Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
  • 8+ years of experience in software engineering, data engineering, or a related field, with at least 3 years specifically in MLOps.
  • Strong programming skills in languages such as Python, Java, or C/C++.
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Proficiency in DevOps tools and practices, including Docker, Kubernetes, and CI/CD pipelines.
Benefits and Compensation

We offer a comprehensive benefits package, including medical, dental, and vision coverage, as well as retirement plan matching. The estimated salary range for this position is $129,200 - $180,500, depending on skills and experience.



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