Job Description & Details
This is a hands-on engineering role focused on building and scaling generative AI solutions within enterprise cloud environments. If you enjoy wrangling LLMs, setting up RAG pipelines, and deploying microservices on Azure without hiding behind theoretical designs, this position is worth a serious look. It bridges traditional cloud architecture with bleeding-edge AI tooling.
What You'll Actually Be Doing
Your day-to-day will revolve around architecting and maintaining production-grade applications that leverage Azure OpenAI and related AI foundry services. You'll spend plenty of time setting up containerized workloads using Docker and Kubernetes, ensuring secure integrations via OAuth 2.0 and OpenID Connect, and integrating AI coding assistants like GitHub Copilot and Cursor into team workflows. Expect to troubleshoot complex latency issues in RAG architectures and build automated workflows using Azure Logic Apps and Functions.
The Core Tech Stack
To survive here, you need a rock-solid foundation in Microsoft Azure and enterprise cloud integrations. The role leans heavily on Azure OpenAI, Generative AI frameworks, LLMs, and RAG pipelines, so knowing how to orchestrate these components efficiently is non-negotiable. You also need fluency in containerization (Docker, Kubernetes) and CI/CD pipelines (Azure DevOps, GitHub Actions), alongside microservices architecture and modern auth protocols.
Interview Expectations
Expect the hiring team to grill you on how you handle token limits, latency, and hallucination reduction in production RAG systems. They are secretly looking for someone who doesn't just call APIs blindly, but deeply understands system constraints, cost optimization, and security boundaries in enterprise cloud environments. You should also be prepared to whiteboard a secure microservices integration using OAuth 2.0 and Azure Functions.
Application Advice
Skip the generic template and tailor your resume to put Azure, Kubernetes, and Generative AI right at the top. Make sure to explicitly mention any hands-on experience you have with Azure OpenAI, RAG implementations, and CI/CD tools like GitHub Actions or Azure DevOps, as these are the exact keyword filters the ATS will be hunting for.