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