Machine Learning Ops Engineer (Remote)

This is a remote position.

• ML Lifecycle Management: Manage the lifecycle of machine learning models from development through deployment and monitoring.
  
• Model Tracking and Management: Utilize ML platforms such as MLflow, Weights & Biases, and Langsmith for model tracking and management.
  
• Infrastructure Decision-Making: Make informed ML infrastructure decisions based on a deep understanding of ML modeling techniques, including model choice, data and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation.
  
• Application Development: Write and test application code, develop and validate ML models, and automate tests and deployment to solve complex problems.
  
• Data Pipeline Construction: Build optimized data pipelines to support ML models.
  
• Cloud-Based Architectures: Leverage or construct cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
  
• CI/CD Best Practices: Implement continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure the successful deployment of ML models and application code.
  
• Infrastructure Optimization: Optimize infrastructure for scalability, cost efficiency, and continuous iteration.
  
• Containerization and Orchestration: Use containerization technologies such as Docker and orchestration tools like Kubernetes.
  
• Infrastructure as Code: Employ Infrastructure as Code approaches using tools like Terraform or CloudFormation.
  

Requirements

• Bachelor’s degree
  
• Experience programming with Python, Scala, or Java
  
• Experience designing and building data-intensive solutions in the cloud
  
• On-the-job experience with an industry-recognized ML framework (scikit-learn, PyTorch)
  
• Experience productionizing, monitoring, and maintaining models
  
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform