Job Description

• 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

• 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

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