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
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