Senior Platform Engineer - Azure Data & AI Platform

Job summary

Build and operate a production Azure data and AI platform through hands-on infrastructure and platform engineering. Design secure Azure landing zones and networks, manage Databricks and Unity Catalog, develop MLOps pipelines using Azure ML or MLflow, and establish Terraform-based infrastructure and secure CI/CD practices. The role also includes platform automation, observability, cost governance, and on-call support.

Benefits

Qualifications

• At least five years of platform or infrastructure engineering experience building production platforms.
• Deep Azure knowledge across networking, identity and access management, storage, compute, and PaaS.
• Experience deploying Azure Databricks workspaces, configuring Unity Catalog, optimizing Spark jobs, and managing costs.
• Infrastructure-as-code experience with Terraform or Bicep, including modules and state management.
• Experience building multistage CI/CD pipelines with Azure DevOps or GitHub Actions.
• Containerization experience with application containers and containers for ML or AI models.
• Security-first understanding of defense in depth, least privilege, and network segmentation.
• MLOps fundamentals, including model training, inference, experiment tracking, versioning, and deployment.
• Python or PowerShell experience for automation and platform tooling.
• Experience with observability beyond basic metrics, including distributed tracing and log aggregation.

Responsibilities

• Design and implement Azure landing zones, management groups, and subscription architecture.
• Build and maintain secure, segmented hub-and-spoke network topologies.
• Implement Azure Policy, role-based access control, and governance frameworks.
• Manage identity and access using Entra ID, service principals, and managed identities.
• Configure monitoring, logging, and alerting with Azure Monitor, Log Analytics, and Application Insights.
• Deploy and configure Azure Databricks workspaces and Unity Catalog.
• Support data and AI pipeline development and integrate Databricks with Azure data services.
• Build and maintain CI/CD for Databricks notebooks, jobs, and infrastructure.
• Develop ML deployment pipelines, model registries, and batch or real-time inference endpoints.
• Implement model monitoring, responsible AI guardrails, and reusable infrastructure modules.
• Build secure CI/CD pipelines, developer tools, and self-service platform automation.
• Participate in the on-call rotation for platform incidents.

Skills

Certifications

Travel

Relocation