ML Ops Engineer

Overall 10 years, with 5-8 years of relevant experience in MLOps.
Deep quantitative/programming background with a Bachelor's degree in a highly analytical discipline (Statistics, Economics, Computer Science, Mathematics, Operations Research, etc.).
Responsibilities:

Design and implement cloud solutions; build MLOps on Azure cloud.
Build CI/CD pipelines orchestration using Azure DevOps or similar tools.
Review, refactor, and optimize data science models; manage containerization, deployment, versioning, and monitoring.
Conduct testing, validation, and automation of data science models.
Collaborate with data scientists, data engineers, and architects; document processes.
Requirements:

10 years total, with 5-8 years managing machine learning projects end-to-end; last 18 months focused on MLOps.
Monitoring build and production systems using automated tools.
Proficiency in machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn.
Experience in building pipelines using Synapse and Databricks.
API integration and data feeds with social analytics (Facebook, Instagram, Twitter).
Familiarity with MLOps tools (ModelDB, Kubeflow, Pachyderm, DVC).
Support model builds and deployments for IDE-based models and AutoML tools.
Experience in Databricks, Azure DataLake Gen2, and Unity Catalog.
Understanding of tools used by data scientists and experience with software development and test automation