Job
Description
As an ML Platform Specialist, your role involves designing, implementing, and maintaining robust machine learning infrastructure and workflows using Databricks Lakehouse Platform. Your responsibilities include: - Designing and implementing scalable ML infrastructure on 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 - Working across teams from various suppliers to support IT Provision, system development, and business units Qualifications required for this role: - Bachelors or masters degree in computer science, Machine Learning, Data Engineering, or related field - 3-5 years of experience in ML Ops with expertise in Databricks and/or Azure ML - Advanced proficiency in Databricks Lakehouse Platform - Strong experience with Databricks MLflow for experiment tracking and model management - Expert-level programming skills in Python, with advanced knowledge of PySpark, MLlib, Delta Lake, Azure ML SDK - Deep understanding of Databricks Feature Store and Feature Engineering techniques - Experience with Databricks workflows, job scheduling, and machine learning frameworks compatible with Databricks and Azure ML (TensorFlow, PyTorch, scikit-learn) - Proficiency in cloud platforms like Azure Databricks, Azure DevOps, Azure ML - Exposure to Terraform, ARM/BICEP, distributed computing, and big data processing techniques - Familiarity with Containerisation, WebApps Kubernetes, Cognitive Services, and other MLOps tools would be advantageous Join the team to contribute to continuous improvement and transformation initiatives for MLOps / DataOps in RSA. As an ML Platform Specialist, your role involves designing, implementing, and maintaining robust machine learning infrastructure and workflows using Databricks Lakehouse Platform. Your responsibilities include: - Designing and implementing scalable ML infrastructure on 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 - Working across teams from various suppliers to support IT Provision, system development, and business units Qualifications required for this role: - Bachelors or masters degree in computer science, Machine Learning, Data Engineering, or related field - 3-5 years of experience in ML Ops with expertise in Databricks and/or Azure ML - Advanced proficiency in Databricks Lakehouse Platform - Strong experience with Databricks MLflow for experiment tracking and model management - Expert-level programming skills in Python, with advanced knowledge of PySpark, MLlib, Delta Lake, Azure ML SDK - Deep understanding of Databricks Feature Store and Feature Engineering techniques - Experience with Databricks workflows, job scheduling, and machine learning frameworks compatible with Databricks and Azure ML (TensorFlow, PyTorch, scikit-learn) - Proficiency in cloud platforms like Azure Databricks, Azure DevOps, Azure ML - Exposure to Terraform, ARM/BICEP, distributed computing, and big data processing techniques - Familiarity with Containerisation, WebApps Kubernetes, Cognitive Services, and other MLOps tools would be advantageous Join the team to contribute to continuous improvement and transformation initiatives for MLOps / DataOps in RSA.
