Job
Description
As an ML Platform Specialist, you will play a crucial role in designing, implementing, and maintaining robust machine learning infrastructure and workflows on the Databricks Lakehouse Platform. Your primary responsibilities will include: - Designing and implementing scalable ML infrastructure on the Databricks Lakehouse Platform - Developing and maintaining CI/CD pipelines for machine learning models using Databricks workflows - Creating automated testing and validation processes with Databricks MLflow - Implementing and managing model monitoring systems using Databricks Model Registry and monitoring tools - Collaborating with data scientists, software engineers, and product teams to optimize machine learning workflows - Developing reproducible machine learning environments using Databricks Notebooks and clusters - Implementing advanced feature engineering and management using Databricks Feature Store - Optimizing machine learning model performance with Databricks runtime and optimization techniques - Ensuring data governance, security, and compliance within the Databricks environment - Creating and maintaining comprehensive documentation for ML infrastructure and processes - Contributing to continuous improvement and transformation initiatives for MLOps / DataOps Qualification Required: - Bachelor's or master's degree in computer science, Machine Learning, Data Engineering, or a related field - 3-5 years of experience in ML Ops with expertise in Databricks and/or Azure ML - Proficiency in Python programming with knowledge of PySpark, MLlib, Delta Lake, Azure ML SDK - Understanding of Databricks Feature Store, Feature Engineering techniques, and cloud platforms - Experience with Databricks workflows, job scheduling, and machine learning frameworks like TensorFlow, PyTorch, scikit-learn - Familiarity with cloud platforms such as Azure Databricks, Azure DevOps, Azure ML, Terraform, ARM/BICEP - Knowledge of distributed computing, big data processing techniques, and MLOps tools like Containerization, Kubernetes, Cognitive Services Please note that this position involves collaborating with various teams and suppliers to optimize machine learning workflows and ensure the smooth deployment and scaling of models across the organization.,
