Whilter.AI - Platform Engineer - DevOps & Azure Infrastructure

Platform Engineer / DevOps Specialist

The Platform Engineer anchors Bridgestone's data migration infrastructure. This role is responsible for establishing, securing, and scaling the cloud environment supporting SAP-to-Databricks ingestion loops and Azure AI Foundry implementations. You will build deterministic Infrastructure as Code (IaC), orchestrate high-availability Kubernetes networking, and maintain runtime reliability.

Key Responsibilities:

- Design and maintain production-grade infrastructure configurations utilizing Terraform for comprehensive Azure service deployments.

- Architect and manage secure Azure Kubernetes Service (AKS) environments, building efficient YAML specs, Helm charts, and scheduling strategies.

- Configure complex cloud networking architecture including Azure Virtual WAN (vWAN), secure cross-region hub-spoke routing, and private endpoints for SAP data gateways.

- Construct and maintain multi-stage Azure DevOps CI/CD pipelines to automate container validation, deployment verification, and testing framework hooks.

- Implement production-grade runtime observability grids utilizing Azure Monitor and Log Analytics across distributed OS layers and data nodes.

- Formulate and calibrate liveness and readiness probes to maximize automated container recovery, fault isolation, and stateful tracking.

Technical Requirements:

- Proven engineering background in systems operations and cloud infrastructure automation, deeply rooted in the Microsoft Azure ecosystem.

- Demonstrated mastery of Infrastructure as Code principles using Terraform alongside container orchestration engines (Docker, Kubernetes, Helm).

- Deep understanding of secure cloud networking architectures, including enterprise vWAN parameters, firewalls, and data plane segregation.

- Hands-on expertise implementing comprehensive monitoring frameworks tracking distributed execution pools across Linux environments and database engines.

- Familiarity with deployment pipelines supporting data orchestrators or machine learning execution contexts is a significant asset.

Total Experience: 6 - 9 years