Cloud Engineer

About the Role

We are looking for a hands-on Platform Engineer with practical AI literacy to help build and scale digital factory platforms on Microsoft Azure for various clientele projects. The platform is designed as a self-service, governed, and automation-led foundation that enables product teams to move AI experiments and digital products from sandbox to production faster, safer, and with clearer accountability. This role supports the development of reusable platform capabilities across infrastructure provisioning, CI/CD delivery, AI-assisted development, AI application hardening, identity and access, observability, FinOps, compliance automation, and developer self-service. You will help turn cloud complexity into secure, repeatable golden paths that product teams can consume with minimal friction.

Key Responsibilities

• Design, build, and operate Azure-based platform capabilities that support digital factory teams across sandbox, QA, UAT, and production environments.
• Develop and maintain infrastructure-as-code templates using tools such as Azure Bicep, Terraform, or equivalent, enabling consistent and repeatable environment provisioning.
• Build self-service golden paths for product teams, including environment provisioning, application templates, deployment patterns, access requests, and approved tooling.
• Implement CI/CD pipelines using Azure DevOps, GitHub, or equivalent platforms to automate build, test, release, promotion, and rollback processes.
• Embed security, reliability, and compliance controls into platform workflows through policy-as-code, automated checks, and guardrails.
• Support the hardening of AI-generated or experimental applications into evaluable MVPs by applying secure-by-default architecture, containerization, API management, monitoring, and deployment baselines.
• Implement identity and access controls using Microsoft Entra ID, role-based access control, Privileged Identity Management, Conditional Access, and least-privilege practices.
• Develop observability capabilities using Azure Monitor, Log Analytics, dashboards, and business-relevant platform metrics covering cost, health, delivery, incidents, and compliance.
• Support FinOps and TCO transparency through resource tagging, budget alerts, cost allocation, token-cost tracking, and show back reporting to product owners.
• Collaborate with product owners, developers, security, audit, finance, and leadership stakeholders to ensure platform capabilities are practical, governed, and aligned to business outcomes.
• Document reusable patterns, operating procedures, and platform decisions to improve adoption, maintainability, and knowledge retention.

Required Skills and Experience

• 8+ years of hands-on engineering experience, including significant experience designing, building, managing, and operating cloud infrastructure on Microsoft Azure in enterprise or regulated environments.
• Deep hands-on experience with Azure platform services such as Azure Landing Zones, Azure Policy, Azure Container Apps, Azure App Service, API Management, Azure Monitor, Log Analytics, Defender for Cloud, and Azure Cost Management.
• Extensive hands-on experience with infrastructure-as-code, automated provisioning, and configuration management using Azure Bicep, Terraform, or equivalent tools.
• Extensive experience designing and operating CI/CD and GitOps pipelines using Azure DevOps, GitHub, or similar platforms, including automated build, test, security scanning, deployment promotion, rollback, and release quality gates.
• Strong hands-on experience applying DevSecOps practices and implementing platform guardrails, including secure software delivery, automated quality checks, secrets management, vulnerability scanning, policy-as-code, and compliance controls.
• Strong hands-on experience in identity and access engineering using Microsoft Entra ID, RBAC, Conditional Access, PIM, access reviews, and least-privilege access governance.
• Practical AI literacy and experience using AI-assisted development tools such as Claude, GitHub Copilot, Microsoft Copilot, or equivalent tools to improve software delivery, code quality, documentation, testing, or developer productivity.
• Experience integrating, securing, and operating AI/ML platform services on Azure, including access controls, network security, service configuration, monitoring, and production-readiness controls for AI-enabled applications.
• Working knowledge of Active Directory and enterprise identity integration concepts, including directory services, group-based access, authentication flows, and hybrid identity considerations.
• Working knowledge of modern authentication and authorization standards, including SAML, OpenID Connect, OAuth 2.0, JWT, and related enterprise application integration patterns.
• Working knowledge of containerization, APIs, cloud-native application patterns, and production-readiness practices.
• Experience supporting at least one production cloud-native application or shared platform capability, with responsibility for reliability, monitoring, deployment, or operational support.
• Ability to translate platform architecture into reusable engineering patterns, templates, developer self-service capabilities, and platform services that product teams can consume with minimal friction.
• Strong documentation and communication skills, with the ability to explain technical concepts to both engineering and non-technical stakeholders.
• Comfortable working in an agile, product-oriented environment where platform capabilities are built incrementally and improved through adoption feedback.

Preferred Skills

• Experience with AI application platforms, Azure OpenAI, Microsoft Fabric, Synapse, data products, or AI governance patterns.
• Experience building developer portals, internal platforms, platform APIs, self-service workflows, or golden-path engineering templates.
• Experience with observability, SRE practices, incident response automation, Microsoft Sentinel, alert routing, and service health dashboards.
• Understanding of FinOps practices, cloud cost optimization, TCO modelling, and cost accountability mechanisms.
• Exposure to tools such as Jira, Confluence, GitHub, Azure DevOps, or other modern engineering productivity tools.