Lead Azure Cloud Engineer

Description

We are looking for a seasoned Lead Azure Cloud Engineer to architect, develop, automate, and manage secure, scalable, and cost-effective cloud solutions on Microsoft Azure. Within this position, you will engage directly with Azure infrastructure, containers, DevOps pipelines, Infrastructure as Code, observability, security controls, and AI-driven cloud solutions. You will assist in converting architecture standards into functional platforms, repeatable deployment patterns, automation, and production-ready services. You will partner closely with Cloud Architects, Platform Engineering, DevOps, Security, Networking, Data, AI, and Product teams to provide reliable Azure solutions that enable modern application delivery, containerized workloads, and AI-driven enterprise capabilities. Responsibilities Architect, deploy, and manage Azure cloud infrastructure, covering subscriptions, resource groups, networking, identity, governance, security, and platform services Develop and maintain repeatable Azure deployment patterns for applications, APIs, front-end workloads, microservices, containers, serverless services, data integrations, and AI-driven solutions Deploy and manage Azure Kubernetes Service environments, encompassing node pools, ingress, workload identity, secrets management, autoscaling, networking, monitoring, container registry integration, and security controls Create, sustain, and enhance Infrastructure as Code with Terraform, including reusable modules, multi-environment deployments, automated validation, and CI/CD pipeline integration Architect and deploy CI/CD pipelines with GitHub Actions, Azure DevOps, GitLab CI/CD, or comparable tools Enable 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 across scripting, IaC development, CI/CD pipeline creation, code review, troubleshooting, documentation, and automation Deploy cloud-native solutions using Azure services including App Service, Azure Functions, Logic Apps, Event Grid, Service Bus, Storage, Key Vault, API Management, Azure SQL, Cosmos DB, Azure Monitor, and Application Insights Assist with deploying AI-driven solutions using Azure AI, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related Azure AI services Develop and integrate AI solution components based on patterns including RAG, agentic workflows, multi-agent orchestration, tool/function calling, prompt management, grounding, evaluation, and responsible AI controls Deploy secure integration patterns using managed identities, RBAC, private endpoints, private DNS, network security groups, firewalls, and API gateways Set up and sustain observability with 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 Implement DevSecOps practices including secrets management, dependency scanning, container image scanning, policy validation, secure configuration, and compliance automation Tune 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 fellow team members Collaborate with architects and stakeholders to convert requirements into practical, secure, and maintainable Azure implementations Requirements Extensive hands-on experience architecting, deploying, and managing Azure cloud solutions in enterprise settings Sound knowledge of Azure networking, identity, governance, security, monitoring, and platform services Hands-on 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 Substantial experience with Terraform or other Infrastructure as Code tools, including module development, state management, validation, and multi-environment delivery Substantial 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 Experience with GitHub, pull requests, code reviews, branching strategies, and collaborative engineering workflows Hands-on experience using GitHub Copilot or comparable AI-assisted development tools for infrastructure, automation, scripting, pipeline development, or documentation Experience with Azure PaaS and integration services including App Service, Azure Functions, Logic Apps, API Management, Event Grid, Service Bus, Storage, Key Vault, Azure SQL, Cosmos DB, and related services Experience deploying observability using Azure Monitor, Log Analytics, Application Insights, Container Insights, dashboards, alerting, and operational runbooks Knowledge of Azure AI and Generative AI services, particularly Azure OpenAI, Azure AI Search, and AI-driven automation patterns Working knowledge of AI architecture patterns including RAG, agentic workflows, multi-agent systems, tool/function calling, grounding, prompt management, evaluation, and responsible AI Capacity to resolve complex technical issues spanning cloud infrastructure, networking, containers, CI/CD, security, and application integration Strong scripting and automation abilities using PowerShell, Bash, Python, or similar languages Capacity to operate independently, own technical delivery, and support production-grade cloud environments Excellent communication abilities and capacity to collaborate with architects, engineers, security teams, product teams, and business stakeholders Nice to have Microsoft Azure certifications including Azure Administrator Associate, Azure Developer Associate, Azure DevOps Engineer Expert, or Azure Solutions Architect Expert Kubernetes certifications including CKA, CKAD, or CKS Experience with production-grade AKS platforms, service mesh, GitOps, Helm, Kustomize, Flux, Argo CD, or Kubernetes policy engines Experience with Azure AI Foundry, Semantic Kernel, LangChain, LangGraph, AutoGen, or similar AI orchestration frameworks Experience developing or supporting RAG platforms, AI agents, multi-agent workflows, or enterprise knowledge search solutions Experience with platform engineering, internal developer platforms, self-service cloud capabilities, paved roads, and reusable engineering templates Experience in regulated industries with rigorous compliance, security, auditability, and governance requirements Familiarity with SRE practices, incident response, reliability engineering, performance testing, and cost optimization

Requirements

• Extensive hands-on experience architecting, deploying, and managing Azure cloud solutions in enterprise settings

• Sound knowledge of Azure networking, identity, governance, security, monitoring, and platform services

• Hands-on 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

• Substantial experience with Terraform or other Infrastructure as Code tools, including module development, state management, validation, and multi-environment delivery

• Substantial 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

• Experience with GitHub, pull requests, code reviews, branching strategies, and collaborative engineering workflows

• Hands-on experience using GitHub Copilot or comparable AI-assisted development tools for infrastructure, automation, scripting, pipeline development, or documentation

• Experience with Azure PaaS and integration services including App Service, Azure Functions, Logic Apps, API Management, Event Grid, Service Bus, Storage, Key Vault, Azure SQL, Cosmos DB, and related services

• Experience deploying observability using Azure Monitor, Log Analytics, Application Insights, Container Insights, dashboards, alerting, and operational runbooks

• Knowledge of Azure AI and Generative AI services, particularly Azure OpenAI, Azure AI Search, and AI-driven automation patterns

• Working knowledge of AI architecture patterns including RAG, agentic workflows, multi-agent systems, tool/function calling, grounding, prompt management, evaluation, and responsible AI

• Capacity to resolve complex technical issues spanning cloud infrastructure, networking, containers, CI/CD, security, and application integration

• Strong scripting and automation abilities using PowerShell, Bash, Python, or similar languages

• Capacity to operate independently, own technical delivery, and support production-grade cloud environments

• Excellent communication abilities and capacity to collaborate with architects, engineers, security teams, product teams, and business stakeholders

Responsibilities

• Architect, deploy, and manage Azure cloud infrastructure, covering subscriptions, resource groups, networking, identity, governance, security, and platform services

• Develop and maintain repeatable Azure deployment patterns for applications, APIs, front-end workloads, microservices, containers, serverless services, data integrations, and AI-driven solutions

• Deploy and manage Azure Kubernetes Service environments, encompassing node pools, ingress, workload identity, secrets management, autoscaling, networking, monitoring, container registry integration, and security controls

• Create, sustain, and enhance Infrastructure as Code with Terraform, including reusable modules, multi-environment deployments, automated validation, and CI/CD pipeline integration

• Architect and deploy CI/CD pipelines with GitHub Actions, Azure DevOps, GitLab CI/CD, or comparable tools

• Enable 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 across scripting, IaC development, CI/CD pipeline creation, code review, troubleshooting, documentation, and automation

• Deploy cloud-native solutions using Azure services including App Service, Azure Functions, Logic Apps, Event Grid, Service Bus, Storage, Key Vault, API Management, Azure SQL, Cosmos DB, Azure Monitor, and Application Insights

• Assist with deploying AI-driven solutions using Azure AI, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related Azure AI services

• Develop and integrate AI solution components based on patterns including RAG, agentic workflows, multi-agent orchestration, tool/function calling, prompt management, grounding, evaluation, and responsible AI controls

• Deploy secure integration patterns using managed identities, RBAC, private endpoints, private DNS, network security groups, firewalls, and API gateways

• Set up and sustain observability with 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

• Implement DevSecOps practices including secrets management, dependency scanning, container image scanning, policy validation, secure configuration, and compliance automation

• Tune 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 fellow team members

• Collaborate with architects and stakeholders to convert requirements into practical, secure, and maintainable Azure implementations

Seniority

• Lead

Nice to have

• Microsoft Azure certifications including Azure Administrator Associate, Azure Developer Associate, Azure DevOps Engineer Expert, or Azure Solutions Architect Expert

• Kubernetes certifications including CKA, CKAD, or CKS

• Experience with production-grade AKS platforms, service mesh, GitOps, Helm, Kustomize, Flux, Argo CD, or Kubernetes policy engines

• Experience with Azure AI Foundry, Semantic Kernel, LangChain, LangGraph, AutoGen, or similar AI orchestration frameworks

• Experience developing or supporting RAG platforms, AI agents, multi-agent workflows, or enterprise knowledge search solutions

• Experience with platform engineering, internal developer platforms, self-service cloud capabilities, paved roads, and reusable engineering templates

• Experience in regulated industries with rigorous compliance, security, auditability, and governance requirements

• Familiarity with SRE practices, incident response, reliability engineering, performance testing, and cost optimization