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# Senior Azure Cloud Engineer

- **Company:** EPAM Systems, Inc.
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Kubernetes Security, Application Programming Interfaces (APIs), Artificial Intelligence, Application Integration Architecture, Application Performance Management, Application Release Automation, Audit Trail, Build Automation, Automation of Tests, Microsoft Azure, Bash Shell, Cloud Computing, Code Review, Continuous Integration, Data Integration, DevOps, Domain Name System (DNS), Github, IP Routing, Python (Programming Language), Key Management, Network Security, Log Analysis, SQL Azure, Octopus Deploy, Windows PowerShell, Role-Based Access Control, Reliability Engineering, Site Reliability Engineering Practices, Cloud Services, Azure Machine Learning, Search Technologies, Software Deployment, Systems Integration, Scripting, Software Modules, Performance Testing, Microsoft Power Automate, Cloud Monitoring, GitHub Copilot, Autoscaling, Delivery Pipeline, Multi-Agent Systems, IT Architecture, Software Troubleshooting, Firewalls (Computer Science), Containerization, Gitlab-ci, Git Flow, Kubernetes, Infrastructure Automation Frameworks, Cosmos DB, Azure AKS, Front End Software Development, Azure Service Fabric, Api Gateway, Terraform, Data Pipelines, Dynatrace, Devsecops, Api Management, Serverless Computing, Key Vault, Vulnerability Analysis, Microservices
- **Published:** August 14, 2026
- **Apply:** https://www.dice.com/job-detail/594bf7d5-9944-48ce-b742-52c543d60d3d

## About the Role

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 Optimize Azure environments for performance, reliability, scalability, and cost efficiency Contribute to technical standards, reusable templates, documentation, operational procedures, and platform engineering practices Provide technical guidance, mentoring, code reviews, and engineering leadership to other team members Work with architects and stakeholders to translate requirements into practical, secure, and maintainable Azure implementations Requirements Strong hands-on experience designing, implementing, and operating Azure cloud solutions in enterprise environments Solid understanding of Azure networking, identity, governance, security, monitoring, and platform services Practical experience with Azure Landing Zone concepts, hub-and-spoke
networking, private endpoints, private DNS, firewalls, NSGs, route tables, and workload integration patterns Strong hands-on experience with Azure Kubernetes Service, containers, container registries, ingress controllers, workload identity, autoscaling, monitoring, and container security Strong experience with Terraform or other Infrastructure as Code tools, including module development, state management, validation, and multi-environment delivery Strong experience with CI/CD pipelines, preferably using GitHub Actions, Azure DevOps, GitLab CI/CD, or similar platforms Good understanding of DevOps and DevSecOps practices, including automated testing, security scanning, artifact management, release automation, and deployment governance Experience working with GitHub, pull requests, code reviews, branching strategies, and collaborative engineering workflows Practical experience using GitHub Copilot or similar AI-assisted development tools for infrastructure, automation, scripting, pipeline
development, or documentation Experience 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 Experience implementing observability using Azure Monitor, Log Analytics, Application Insights, Container Insights, dashboards, alerting, and operational runbooks Understanding of Azure AI and Generative AI services, especially Azure OpenAI, Azure AI Search, and AI-enabled automation patterns Practical knowledge of AI architecture patterns such as RAG, agentic workflows, multi-agent systems, tool/function calling, grounding, prompt management, evaluation, and responsible AI Ability to troubleshoot complex technical issues across cloud infrastructure, networking, containers, CI/CD, security, and application integration Strong scripting and automation skills using PowerShell, Bash, Python, or similar languages Ability to work independently, take ownership of
technical delivery, and support production-grade cloud environments Strong communication skills and ability to work 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 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 building 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 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

## Description

We are seeking an experienced Senior Azure Cloud Engineer to design, build, automate, and operate secure, scalable, and cost-efficient cloud solutions on Microsoft Azure. In this role, you will work hands-on with Azure infrastructure, containers, DevOps pipelines, Infrastructure as Code, observability, security controls, and AI-enabled cloud solutions. You will help translate architecture standards into working platforms, reusable deployment patterns, automation, and production-ready services. You will collaborate closely with Cloud Architects, Platform Engineering, DevOps, Security, Networking, Data, AI, and Product teams to deliver robust Azure solutions that support modern application delivery, containerized workloads, and AI-enabled enterprise capabilities. Responsibilities Design, implement, and maintain Azure cloud infrastructure, including subscriptions, resource groups, networking, identity, governance, security, and platform services Build and support reusable Azure deployment
patterns for applications, APIs, front-end workloads, microservices, containers, serverless services, data integrations, and AI-enabled solutions Implement and operate Azure Kubernetes Service environments, including node pools, ingress, workload identity, secrets management, autoscaling, networking, monitoring, container registry integration, and security controls Build, maintain, and improve Infrastructure as Code using Terraform, including reusable modules, multi-environment deployments, automated validation, and integration with CI/CD pipelines Design and implement CI/CD pipelines using GitHub Actions, Azure DevOps, GitLab CI/CD, or similar tools Support DevOps practices such as automated builds, testing, security scanning, artifact management, environment promotion, deployment approvals, rollback strategies, and release automation Use GitHub Copilot and AI-assisted engineering tools to improve productivity across scripting, IaC development, CI/CD pipeline creation, code review,
troubleshooting, documentation, and automation Implement 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 the implementation of AI-enabled solutions using Azure AI, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related Azure AI services Build 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 using managed identities, RBAC, private endpoints, private DNS, network security groups, firewalls, and API gateways Establish and maintain observability using Azure Monitor, Log Analytics, Application Insights, Container Insights, dashboards, alerts, distributed tracing, and operational runbooks Troubleshoot complex cloud


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