MLOps Engineer - Cloud Solutions (6-12 yrs)

Experience Required : Overall, 6+ years out of 4+ years relevant experience in MLOps.

Qualification : Deep quantitative/programming background with a degree (Bachelor's) in a highly analytical discipline, like Statistics, Economics, Computer Science, Mathematics, Operations Research, etc

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.

Requirements :

- Overall 6+ years out of which 4+ 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

- Knowledge on building pipelines using synapse, data bricks end to end.

- Experience in API integration and data feeds with social analytics(Facebook, Instagram, Twitter).

- 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) will be an addon, model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard)

- Experience in Databricks, Azure DataLake Gen2 and Unity Catalog

- Ability to understand tools used by data scientist and experience with software development and test automation.