ML Platform Specialist

The ML Platform Specialist role involves designing, implementing, and maintaining machine learning infrastructure and workflows on the Databricks Lakehouse Platform. Your primary responsibility will be to ensure the successful deployment, monitoring, and scaling of machine learning models across the organization. You will design and implement scalable ML infrastructure on the Databricks Lakehouse Platform, develop CI/CD pipelines for machine learning models, and create automated testing and validation processes using Databricks MLflow. Additionally, you will be responsible for managing model monitoring systems, collaborating with various teams to optimize machine learning workflows, and maintaining reproducible machine learning environments using Databricks Notebooks and clusters. Furthermore, you will implement advanced feature engineering and management using the Databricks Feature Store, optimize machine learning model performance, and ensure data governance, security, and compliance within the Databricks environment. Your role will also involve creating and maintaining comprehensive documentation for ML infrastructure and processes, as well as working across teams from multiple suppliers to drive continuous improvement and transformation initiatives for MLOps/DataOps. To be successful in this role, you should have a Bachelor's or Master's degree in computer science, Machine Learning, Data Engineering, or a related field, along with 3-5 years of experience in ML Ops with expertise in Databricks and/or Azure ML. You should possess advanced proficiency with the Databricks Lakehouse Platform, strong experience with Databricks MLflow, and expert-level programming skills in Python, including knowledge of PySpark, MLlib, Delta Lake, and Azure ML SDK. Moreover, you should have a deep understanding of Databricks Feature Store and Feature Engineering techniques, experience with Databricks workflows and job scheduling, and proficiency in machine learning frameworks compatible with Databricks and Azure ML such as TensorFlow, PyTorch, and scikit-learn. Knowledge of cloud platforms like Azure Databricks, Azure DevOps, and Azure ML, as well as exposure to Terraform, ARM/BICEP, distributed computing, and big data processing techniques, will be essential for this role. Experience with Containerization, WebApps Kubernetes, Cognitive Services, and other MLOps tools will be considered a plus, as you contribute to the continuous improvement and transformation of MLOps/DataOps in the organization.,