Job Description

Key Responsibilities

Architect and maintain cloud environments to meet user needs with high reliability and security
Monitor and manage core DevOps resources to ensure optimal performance
Oversee enterprise data lake and Databricks environments, including storage strategies, archiving, and uptime monitoring
Build and maintain infrastructure across Azure, AWS, and other platforms to support AI initiatives
Design and manage CI/CD pipelines for machine learning workflows
Implement model versioning and reproducibility standards using tools like MLflow or Weights & Biases
Automate deployment using containerization (Docker, Kubernetes) and orchestration frameworks
Monitor production ML systems for performance, drift, and anomalies
Collaborate across teams to ensure seamless integration of ML operations
Apply infrastructure-as-code and DevSecOps best practices
Support governance, compliance, and data privacy frameworks for AI systems

Required Qualifications

5+ years of experience in cloud infrastructure or MLOps roles
Proficiency with AWS, Azure, or similar cloud platforms
Hands-on experience with Databricks
Skilled in infrastructure-as-code tools (e.g., Terraform, Pulumi, AWS CDK)
Strong background in containerization and orchestration technologies
Experience building CI/CD pipelines for ML systems
Proficient in scripting languages such as Python or Bash
Familiarity with ML lifecycle tools and observability platforms

Job Tags

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