Description

You will manage the end-to-end machine learning lifecycle on Azure cloud.

Responsibilities

• Design and implement cloud solutions and MLOps workflows on Azure.
• Build and orchestrate CI/CD pipelines using Azure DevOps or similar tools.
• Review, refactor, and optimize data science models while managing containerization, deployment, versioning, and monitoring.
• Conduct testing, validation, and automation of data science models.
• Collaborate with data scientists, data engineers, and architects to document processes.

Required Skills

• 10 years of total professional experience, with 5-8 years managing machine learning projects end-to-end.
• At least 18 months of recent, focused experience in MLOps.
• Bachelor's degree in an analytical discipline such as Statistics, Economics, Computer Science, Mathematics, or Operations Research.
• Proficiency with machine learning frameworks including TensorFlow, PyTorch, Keras, and Scikit-Learn.
• Experience building pipelines using Azure Synapse and Databricks.
• Hands-on experience with Azure DataLake Gen2 and Unity Catalog.
• Competency in monitoring build and production systems using automated tools.
• Experience with API integration and data feeds for social analytics (Facebook, Instagram, Twitter).
• Strong background in software development and test automation.

Preferred Skills

• Familiarity with MLOps tools such as ModelDB, Kubeflow, Pachyderm, or DVC.
• Experience supporting model builds for IDE-based models and AutoML tools.

Key Skills

Education

Bachelor's degree