ML OPS Engineer

Overall 10 years out of 5-8 years relevant experience in MLOps.

Azure Terraform IAC, ML Flow and ML re-training pipelines

can support the team with deploying LLMs, ML inference APIs in Azure or Databricks

Can deploy monitoring and full set of observability set.

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 10 years out of 5-8 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, Data robot, Kubeflow, MLflow, neptune.ai) will be an addon, model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard)

Experience in Databricks, Azure Data Lake Gen2 and Unity Catalog

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