Lead Azure Cloud Engineer

Quick Facts

Lead Azure Cloud Engineer role focused on architecting, developing, automating, and operating secure, scalable, and cost-effective cloud solutions on Microsoft Azure.

Description

As a Lead Azure Cloud Engineer, you will architect, develop, automate, and manage Azure cloud solutions that are production-ready and optimized for security and cost. You will work with Azure infrastructure, AKS, DevOps pipelines, Infrastructure as Code, observability, security controls, and AI-driven cloud capabilities. You will translate architecture standards into repeatable deployment patterns and functional platforms.

Responsibilities

• Architect, deploy, and manage Azure cloud infrastructure (subscriptions, resource groups, networking, identity, governance, security, platform services)
• Create repeatable Azure deployment patterns for applications, APIs, microservices, containers, serverless, data integrations, and AI-driven solutions
• Deploy and manage Azure Kubernetes Service (node pools, ingress, workload identity, secrets management, autoscaling, networking, monitoring, registry integration, security controls)
• Build and enhance Infrastructure as Code with Terraform (reusable modules, multi-environment deployments, automated validation, CI/CD integration)
• Architect and deploy CI/CD pipelines using GitHub Actions, Azure DevOps, GitLab CI/CD, or comparable tools
• Implement DevOps and DevSecOps practices: automated builds/testing, security scanning, artifact management, environment promotion, approvals, rollback, and release automation
• Use GitHub Copilot and AI-assisted engineering tools for scripting, IaC, pipeline creation, code review, troubleshooting, documentation, and automation
• Deploy cloud-native workloads with Azure PaaS and integration services (App Service, Functions, Logic Apps, Event Grid, Service Bus, Storage, Key Vault, API Management, Azure SQL, Cosmos DB, Monitor, Application Insights)
• Support AI-driven solutions using Azure AI (Azure OpenAI, Azure AI Search, Azure Machine Learning)
• Develop AI components using RAG, agentic workflows, multi-agent orchestration, tool/function calling, prompt management, grounding, evaluation, and responsible AI controls
• Implement secure integration patterns with managed identities, RBAC, private endpoints, private DNS, NSGs, firewalls, and API gateways
• Set up observability with Azure Monitor/Log Analytics/Application Insights/Container Insights, dashboards, alerts, distributed tracing, and operational runbooks
• Resolve complex incidents across cloud infrastructure, networking, deployment, performance, security, and production operations
• Apply DevSecOps including secrets management, dependency and container scanning, policy validation, secure configuration, and compliance automation
• Tune Azure environments for performance, reliability, scalability, and cost efficiency
• Contribute to standards, reusable templates, documentation, operational procedures, and platform engineering practices
• Provide technical guidance, mentoring, and code reviews; collaborate with architects and stakeholders to deliver secure implementations

Requirements

• Extensive hands-on experience architecting, deploying, and managing Azure cloud solutions in enterprise settings
• Strong knowledge of Azure networking, identity, governance, security, monitoring, and platform services
• Hands-on experience with Azure Landing Zone concepts and hub-and-spoke networking (private endpoints, private DNS, firewalls, NSGs, route tables, workload integration)
• Extensive hands-on experience with AKS, containers, container registries, ingress, workload identity, autoscaling, monitoring, and container security
• Substantial Terraform (or similar) Infrastructure as Code experience (modules, state management, validation, multi-environment delivery)
• Substantial CI/CD pipeline experience (GitHub Actions, Azure DevOps, GitLab CI/CD, or similar)
• Solid DevOps and DevSecOps knowledge (automated testing, security scanning, artifact management, release automation, deployment governance)
• GitHub experience including pull requests, code reviews, and branching strategies
• Hands-on experience with GitHub Copilot or comparable AI-assisted development tools
• Experience with Azure PaaS and integration services: App Service, Azure Functions, Logic Apps, API Management, Event Grid, Service Bus, Storage, Key Vault, Azure SQL, Cosmos DB
• Experience deploying observability with Azure Monitor, Log Analytics, Application Insights, Container Insights, dashboards, alerting, and runbooks
• Knowledge of Azure AI and Generative AI services, especially Azure OpenAI and Azure AI Search
• Working knowledge of AI patterns (RAG, agentic workflows, multi-agent systems, tool/function calling, grounding, prompt management, evaluation, responsible AI)
• Ability to troubleshoot complex problems across cloud infrastructure, networking, containers, CI/CD, security, and application integration
• Strong scripting and automation skills (PowerShell, Bash, Python, or similar)
• Ability to work independently, own delivery, and support production-grade cloud environments
• Excellent communication and collaboration with architects, engineers, security, product teams, and stakeholders

Benefits

• Hybrid-by-design work model with opportunity to work remotely within Poland
• Work abroad opportunity up to 60 days annually; business-driven relocation opportunities
• Career development programs, mentoring, and soft skills/well-being programs
• Certification support (Anthropic, Gemini, GCP, Azure, AWS) and English classes
• Stable pay; Employee Stock Purchase Plan with a 15% discount
• Benefits package (health insurance, multisport, shopping vouchers) and referral bonuses up to $2,000
• Office perks and corporate/social/well-being events
• Note: benefits apply to employees only; contractors are possible under individually agreed B2B terms