Job Description & Details
This is a heavy-hitting cloud engineering role where you will be building resilient enterprise applications while aggressively integrating modern AI workflows into the SDLC. If you are tired of legacy maintenance and want to build cloud-native systems that actually leverage modern tooling, this gig is worth a serious look.
What You'll Actually Be Doing
Your day-to-day will involve designing, building, and supporting scalable APIs, integrations, and cloud-native solutions from scratch. You aren't just writing code; you are actively using tools like GitHub Copilot and Azure AI Foundry to accelerate development, implement automated testing, and optimize production monitoring. Expect to collaborate closely with architects to translate tough business logic into production-ready implementations while driving modernization initiatives across the team.
The Core Tech Stack
To survive here, you need deep, battle-tested experience with modern .NET, C#, and Azure services like App Services, Functions, API Management, and Service Bus. But what really sets this role apart is the required fluency in AI-assisted software engineering—you need to know your way around prompt engineering, AI-enabled testing, and developer productivity workflows. They are leaning hard into the AI-augmented ecosystem, so superficial knowledge won't cut it.
Interview Expectations
Be ready for a deep dive into Azure distributed system design and how you handle failure scenarios in microservices. The interviewer will likely ask you to explain a time you optimized a slow query on Azure SQL and managed async messaging through a Service Bus. They want to hear about your systematic approach to observability and cost management in the cloud, so bring concrete examples of architectural trade-offs you've made in the past.
Application Advice
Your resume needs to scream modern .NET and Azure architecture right at the top. Make sure to explicitly highlight any hands-on AI development work you've done, whether that's integrating OpenAI APIs or using GitHub Copilot to measure productivity lifts. Mirror the keywords from the job description like Azure App Services, CI/CD pipelines, and automated testing to clear the ATS, but back them up with hard metrics in your bullet points.