ML Ops Engineer

Enable Data Incorporated is a leading provider of advanced application, data, and cloud engineering services focused on offering modern solutions that help customers drive increased value across their business ecosystem. We are currently seeking a Machine Learning Ops Engineer to join our team. This exciting position will be responsible for using machine learning technologies to help our clients build highly-scalable and sophisticated applications.

Responsibilities

• Design and implement cloud solutions, build MLOps on Azure cloud
• Build CI/CD pipelines orchestration by Azure devops or similar tools
• Data science model review, code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
• Data science models testing, validation and tests automation
• Communicate with a team of data scientists, data engineers and architect, document the processes

• Deep quantitative/programming background with a degree (Bachelor’s, Master’s, or Ph.D.) in a highly analytical discipline, like Statistics, Economics, Computer Science, Mathematics, Operations Research, etc.
• Total of 3-6 years of experience in managing machine learning projects end-to-end, with the last 18 months focused on MLOps.
• Monitoring Build & Production systems using automated monitoring and alarm tools.
• Knowledge of machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn.
• Experience with MLOps tools such as ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC).
• Experience in supporting model builds and model deployment for IDE-based models and autoML tools, experiment tracking, model management, version tracking & model training (Dataiku, Datarobot, Kubeflow, MLflow, neptune.ai), model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard).
• Experience in Databricks Lakehouse and Unity Catalog
• Ability to understand tools used by data scientist and experience with software development and test automation