DevOps (MLOps) Engineer

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Requirements:

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field.
• 5+ years of experience in DevOps or MLOps roles.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Proficiency in programming languages such as Python, R, or Java.
• Strong understanding of cloud platforms (AWS, Azure, or GCP) and their machine learning services.
• Experience with containerization technologies, particularly Docker, and orchestration tools like Kubernetes.
• Knowledge of CI/CD tools like Jenkins, GitLab CI/CD, or Azure DevOps.
• Familiarity with data pipeline tools and frameworks, such as Apache Airflow or Kubeflow.
• Strong problem-solving and analytical skills.
• Excellent communication and teamwork abilities.

Responsibilities:

• Design, implement, and manage MLOps pipelines for deploying and monitoring machine learning models.
• Collaborate with data scientists to understand model requirements and operationalize models into production environments.
• Automate model training, testing, and deployment processes through CI/CD principles.
• Monitor and evaluate model performance in production and implement solutions for improvement.
• Implement data versioning, model versioning, and tracking systems to ensure reproducibility.
• Ensure security and compliance in the deployment of machine learning models.
• Document processes and improve collaboration between teams for efficient workflows.
• Stay up-to-date with industry trends and technologies in MLOps and machine learning.