Cloud Engineer - Mechanicsburg, PA - F2F Interview - Locals Only

Role Description

• 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.