Machine Learning Operations Engineer

4 weeks ago

Montreal, Quebec, Canada 2K Full time
Data & Analytics Engineer

At 2K, we're looking for a skilled Data & Analytics Engineer to join our team. As a key member of our Data & Analytics function, you'll be responsible for designing and implementing machine learning systems that bring both quantitative and qualitative insights to our entire company.

We're seeking a talented individual with a passion for creating advanced analytics data products to allow partners to make crucial decisions. If you have a strong background in machine learning, data engineering, and cloud computing, and you're comfortable working in an Agile environment, we want to hear from you.

Responsibilities:
  1. Collaborate with cross-functional teams to design and implement machine learning systems that drive business outcomes.
  2. Maintain scalable ML infrastructure across multiple regions to ensure high-performance and reliability.
  3. Develop and implement ML pipelines for automating and accelerating ML deployment tasks.
  4. Build end-to-end ML lifecycle management processes, including data extraction and processing, model training and versioning, hyperparameter tuning, evaluation, and deployment.
  5. Implement robust software best practices, such as coding standards, code reviews, source control management, build processes, testing, and operations.
  6. Monitor and improve the performance of machine learning models in production.
  7. Contribute to the development of best practices for our MLOps strategies.
  8. Stay updated on developments in MLOps technologies and frameworks.
Qualifications:
  1. Bachelor's degree in Computer Science, Data Science, or related field.
  2. Experience as an MLOps Engineer or similar role in Machine Learning.
  3. Understanding of cloud computing, data management, and machine learning algorithms.
  4. Proficiency with programming languages such as Python, R, or Scala.
  5. Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).
  6. Experience with infrastructure automation technologies like Docker, Kubernetes, Terraform, etc.
  7. Must have a working knowledge of CI/CD principles and tools, including GitHub, GitLab, and Jenkins.
  8. Knowledge of data structures, data modeling, and software architecture.
  9. Understanding of data analysis workflows and machine learning model lifecycle.
  10. Comfortable working in an Agile environment.

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