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 DecisionMaking: 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.
  
• CloudBased Architectures: Leverage or construct cloudbased 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 dataintensive solutions in the cloud
  
• Onthejob experience with an industryrecognized ML framework (scikitlearn 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