Description

Design and maintain large-scale Azure Databricks environments, automate MLOps workflows, and manage cloud infrastructure for stable system performance.

Design, develop, and implement large Databricks environments in collaboration with stakeholder teams.

• Facilitate Machine Learning models using Spark, PySpark, and PyTorch within distributed computing ecosystems.
• Develop Infrastructure as Code (IaC) to automate environment deployments and ensure reproducibility.
• Create and maintain a library of scripts to automate Databricks MLOps environments.
• Perform production deployments and model monitoring to ensure stable system performance.

Responsibilities

• Design and implement large-scale Databricks environments.
• Automate MLOps workflows using IaC and scripting libraries.
• Manage cloud infrastructure and security guidelines for Databricks.
• Deploy and monitor Machine Learning models in production.
• Configure ML platforms to meet specific product requirements.

Required Skills

• 5+ years of experience developing and implementing models in cloud ecosystems (Azure, AWS, or GCP).
• Hands-on experience with Databricks, Spark, and SageMaker or similar distributed computing technologies.
• Proficiency in developing Infrastructure as Code (IaC).
• Strong understanding of Azure resources including containers, virtual machines, storage, log analytics, alerting, metrics, Azure AD, Key Vaults, and Azure Backup.
• Bachelor's degree.
• Strong written and verbal communication skills.

Key Skills

Education

Bachelor's degree