What are the responsibilities and job description for the DevOps Engineer position at Jobs via Dice?
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Design, develop, implement, and support cloud-native applications, services, and APIs using technologies such as .NET, .NET Core, containers, Python, and related tools, as appropriate to the solution.
Design, implement, and support secure, scalable, and reliable solutions in Azure and AWS environments.
Deploy and manage cloud infrastructure and platform services in support of application modernization, integration, and enterprise solution delivery.
Support cloud migration, integration, and modernization efforts across a range of PSDC business processes.
Work with Azure services such as App Service, Service Bus, API Management, Logic Apps, Azure Functions, Azure SQL Managed Instance, Azure SQL Database, Blob Storage, File Storage, and Table Storage.
Work with Azure infrastructure and networking components such as Virtual Machines, Virtual Networks, Network Security Groups, VPN Gateway, and ExpressRoute.
Work with AWS services such as EC2, S3, VPC, Lambda, and RDS in support of secure and reliable cloud solutions.
Support containerization and orchestration using technologies such as Docker, Kubernetes, AWS ECS, Azure AKS, Azure Container Apps, and similar platforms.
Support CI/CD pipelines and DevOps practices using tools such as Azure DevOps, GitHub, Artifactory, and NuGet.
Apply software engineering, software architecture, SDLC, and data management practices throughout the delivery lifecycle.
Contribute to technical solution design, implementation planning, engineering analysis, and related business and technical review activities.
Apply knowledge of networking and infrastructure concepts, including DNS, Active Directory, firewalls, load balancers, and virtual networking.
Support GPT-enabled solution capabilities, including prompt engineering, intelligent chatbots, document summarization, semantic search, and related business use cases.
Apply awareness of token usage, cost management, performance optimization, content filtering, and compliance considerations when deploying GPT-based solutions in production cloud environments, including government and public safety contexts.
Design, develop, implement, and support cloud-native applications, services, and APIs using technologies such as .NET, .NET Core, containers, Python, and related tools, as appropriate to the solution.
Design, implement, and support secure, scalable, and reliable solutions in Azure and AWS environments.
Deploy and manage cloud infrastructure and platform services in support of application modernization, integration, and enterprise solution delivery.
Support cloud migration, integration, and modernization efforts across a range of PSDC business processes.
Work with Azure services such as App Service, Service Bus, API Management, Logic Apps, Azure Functions, Azure SQL Managed Instance, Azure SQL Database, Blob Storage, File Storage, and Table Storage.
Work with Azure infrastructure and networking components such as Virtual Machines, Virtual Networks, Network Security Groups, VPN Gateway, and ExpressRoute.
Work with AWS services such as EC2, S3, VPC, Lambda, and RDS in support of secure and reliable cloud solutions.
Support containerization and orchestration using technologies such as Docker, Kubernetes, AWS ECS, Azure AKS, Azure Container Apps, and similar platforms.
Support CI/CD pipelines and DevOps practices using tools such as Azure DevOps, GitHub, Artifactory, and NuGet.
Apply software engineering, software architecture, SDLC, and data management practices throughout the delivery lifecycle.
Contribute to technical solution design, implementation planning, engineering analysis, and related business and technical review activities.
Apply knowledge of networking and infrastructure concepts, including DNS, Active Directory, firewalls, load balancers, and virtual networking.
Support GPT-enabled solution capabilities, including prompt engineering, intelligent chatbots, document summarization, semantic search, and related business use cases.
Apply awareness of token usage, cost management, performance optimization, content filtering, and compliance considerations when deploying GPT-based solutions in production cloud environments, including government and public safety contexts.