Software Engineer II - Coreai

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Software Engineering

Software Engineer II on the Azure Dev Services team, focusing on building and operating scalable, low-latency distributed systems. The role involves integrating AI-assisted development tools and contributing to AI-enhanced features using LLMs, MCP servers, and RAG. Experience with cloud platforms and AI-powered solutions in production is preferred.

What you'd actually do

  1. Own and operate highly scalable, reliable, and low-latency distributed systems that power mission-critical workloads.
  2. Design and implement features that enable configuration management, monitoring, analytics, and observability for modern cloud applications.
  3. Drive integration with other Azure services to deliver seamless customer experiences.
  4. Write high-quality, well-tested code and own the DevOps lifecycle, including monitoring, alerting, and incident response.
  5. Integrate AI-assisted development tools to improve engineering productivity and code quality.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field
  • 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

Nice to have

  • Master's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
  • 1+ years of proficiency with AI-assisted development tools (e.g., GitHub Copilot, IntelliCode)
  • Experience with cloud platforms such as Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP)
  • Experience with .NET
  • Experience developing, deploying, or integrating AI-powered solutions in production environments
  • Experience building or integrating AI applications using technologies such as LLMs, Model Context Protocol (MCP) servers, or RAG pipelines
  • Working knowledge of web and cloud technologies, including containers, microservices, and CI/CD pipelines
  • Familiarity with distributed systems, cloud-native architectures, or managed cloud services

What the JD emphasized

  • low-latency systems
  • mission-critical workloads
  • AI-assisted development tools
  • LLMs
  • Model Context Protocol (MCP) servers
  • RAG
  • AI-powered solutions in production environments
  • AI applications

Other signals

  • integrating AI-assisted development tools
  • Contribute to AI-enhanced features using technologies such as Large Language Models (LLMs), Model Context Protocol (MCP) servers, and Retrieval-Augmented Generation (RAG)
  • Experience building or integrating AI-powered solutions in production environments
  • Experience building or integrating AI applications using technologies such as LLMs, Model Context Protocol (MCP) servers, or RAG pipelines