Lead Azure Cloud Engineer

We’re looking for a seasoned Lead Azure Cloud Engineer to architect, construct, automate, and manage secure, scalable, and cost-effective cloud solutions on Microsoft Azure. This role involves direct engagement with Azure infrastructure, containerization, DevOps pipelines, Infrastructure as Code, observability, security controls, and AI-driven cloud solutions. You’ll help convert architecture standards into functioning platforms, repeatable deployment patterns, automation processes, and production-ready services. You’ll partner closely with Cloud Architects, Platform Engineering, DevOps, Security, Networking, Data, AI, and Product teams to deliver strong Azure solutions supporting modern application delivery, containerized workloads, and enterprise AI capabilities. Responsibilities Design, implement, and maintain Azure cloud infrastructure, including subscriptions, resource groups, networking, identity, governance, security, and platform services Construct and support reusable Azure deployment patterns for applications, APIs, front-end workloads, microservices, containers, serverless services, data integrations, and AI-enabled solutions Operate and implement Azure Kubernetes Service environments, covering node pools, ingress, workload identity, secrets management, autoscaling, networking, monitoring, container registry integration, and security controls Develop, maintain, and enhance Infrastructure as Code using Terraform, including reusable modules, multi-environment deployments, automated validation, and CI/CD pipeline integration Create and implement CI/CD pipelines using GitHub Actions, Azure DevOps, GitLab CI/CD, or comparable tools Support DevOps practices including automated builds, testing, security scanning, artifact management, environment promotion, deployment approvals, rollback strategies, and release automation Leverage GitHub Copilot and AI-assisted engineering tools to boost productivity in scripting, IaC development, CI/CD pipeline creation, code review, troubleshooting, documentation, and automation Deploy cloud-native solutions using Azure services such as App Service, Azure Functions, Logic Apps, Event Grid, Service Bus, Storage, Key Vault, API Management, Azure SQL, Cosmos DB, Azure Monitor, and Application Insights Support implementation of AI-enabled solutions using Azure AI, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related Azure AI services Construct and integrate AI solution components based on patterns such as RAG, agentic workflows, multi-agent orchestration, tool/function calling, prompt management, grounding, evaluation, and responsible AI controls Implement secure integration patterns leveraging managed identities, RBAC, private endpoints, private DNS, network security groups, firewalls, and API gateways Set up and maintain observability using Azure Monitor, Log Analytics, Application Insights, Container Insights, dashboards, alerts, distributed tracing, and operational runbooks Resolve complex cloud

networking, deployment, performance, security, and production incidents Apply DevSecOps practices including secrets management, dependency scanning, container image scanning, policy validation, secure configuration, and compliance automation Enhance Azure environments for performance, reliability, scalability, and cost efficiency Contribute to technical standards, reusable templates, documentation, operational procedures, and platform engineering practices Offer technical guidance, mentoring, code reviews, and engineering leadership to team members Collaborate with architects and stakeholders to convert requirements into practical, secure, and maintainable Azure implementations Requirements Extensive hands-on experience architecting, implementing, and managing Azure cloud solutions within enterprise environments Strong grasp of Azure networking, identity, governance, security, monitoring, and platform services Applied experience with Azure Landing Zone concepts, hub-and-spoke networking, private endpoints, private DNS, firewalls, NSGs, route tables, and workload integration patterns Extensive hands-on experience with Azure Kubernetes Service, containers, container registries, ingress controllers, workload identity, autoscaling, monitoring, and container security Extensive experience with Terraform or similar Infrastructure as Code tools, including module development, state management, validation, and multi-environment delivery Extensive experience with CI/CD pipelines, ideally using GitHub Actions, Azure DevOps, GitLab CI/CD, or similar platforms Solid grasp of DevOps and DevSecOps practices including automated testing, security scanning, artifact management, release automation, and deployment governance Background working with GitHub, pull requests, code reviews, branching strategies, and collaborative engineering workflows Applied experience using GitHub Copilot or comparable AI-assisted development tools for infrastructure, automation, scripting, pipeline development, or documentation Background with Azure PaaS and integration services such as App Service, Azure Functions, Logic Apps, API Management, Event Grid, Service Bus, Storage, Key Vault, Azure SQL, Cosmos DB, and related services Background implementing observability using Azure Monitor, Log Analytics, Application Insights, Container Insights, dashboards, alerting, and operational runbooks Grasp of Azure AI and Generative AI services, particularly Azure OpenAI, Azure AI Search, and AI-enabled automation patterns Applied knowledge of AI architecture patterns such as RAG, agentic workflows, multi-agent systems, tool/function calling, grounding, prompt management, evaluation, and responsible AI Capability to resolve complex technical issues spanning cloud infrastructure, networking, containers, CI/CD, security, and application integration Solid scripting and automation abilities using PowerShell, Bash, Python, or comparable languages Capacity to work independently, own technical delivery, and support production-grade cloud environments Strong communication skills and capability to collaborate with architects, engineers, security teams, product teams, and business stakeholders Nice to have Microsoft Azure certifications such as Azure Administrator Associate, Azure Developer Associate, Azure DevOps Engineer Expert, or Azure Solutions Architect Expert Kubernetes certifications such as CKA, CKAD, or CKS Background with production-grade AKS platforms, service mesh, GitOps, Helm, Kustomize, Flux, Argo CD, or Kubernetes policy engines Background with Azure AI Foundry, Semantic Kernel, LangChain, LangGraph, AutoGen, or comparable AI orchestration frameworks Background constructing or supporting RAG platforms, AI agents, multi-agent workflows, or enterprise knowledge search solutions Background with platform engineering, internal developer platforms, self-service cloud capabilities, paved roads, and reusable engineering templates Background working in regulated industries with strong compliance, security, auditability, and governance requirements Familiarity with SRE practices, incident response, reliability engineering, performance testing, and cost optimization