Principal Software Engineer

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

Principal Software Engineer at Microsoft's Commercial Engineering & AI (CEAI) team, focusing on building and operating distributed services and integrating AI capabilities like NLP, semantic search, and intelligent agents using Azure OpenAI and Semantic Kernel. The role involves end-to-end ownership of features, driving architectural decisions, and ensuring performance, scalability, and cost efficiency. It also includes mentoring engineers and contributing to DevOps and live site reliability.

What you'd actually do

  1. Integrate AI capabilities such as natural language processing, semantic search, and intelligent agents using frameworks like Azure OpenAI, Semantic Kernel, or equivalent.
  2. Drive architectural decisions across platform capabilities such as offer publishing, catalog, search, purchase, fulfillment, metering, and billing.
  3. Own significant features and subsystems end to end: requirements, design, implementation, testing, deployment, and live site support.
  4. Mentor engineers across the team, lead technical design reviews, and raise the engineering bar on quality, reliability, and responsible AI practices.
  5. Drive performance, scalability, and cost efficiency through telemetry, asynchronous I/O, and system profiling.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 10+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
  • Design, build, and operate distributed services and microservices on Azure (App Service, AKS, Functions, Service Bus, Cosmos DB, Azure SQL)
  • Own significant features and subsystems end to end
  • Integrate AI capabilities such as natural language processing, semantic search, and intelligent agents using frameworks like Azure OpenAI, Semantic Kernel, or equivalent.
  • Drive performance, scalability, and cost efficiency through telemetry, asynchronous I/O, and system profiling.
  • Ensure robust data validation, schema enforcement, and compliance with privacy and security standards.
  • Collaborate across disciplines to define technical requirements, review designs, and deliver high quality software.
  • Mentor engineers across the team, lead technical design reviews, and raise the engineering bar on quality, reliability, and responsible AI practices.
  • Participate in an on call rotation; investigate and resolve live site incidents, and drive root cause fixes that improve reliability.
  • Contribute to our DevOps culture: CI/CD pipelines, automated testing, telemetry, and safe deployment practices.

Nice to have

  • Proficiency in C#, Python, or JavaScript, and familiarity with cloud platforms like Azure or AWS.
  • Experience designing and consuming REST APIs and working with relational and/or NoSQL data stores.
  • Solid fundamentals in data structures, algorithms, distributed systems, and debugging.
  • Experience with AI development tools and frameworks (e.g., OpenAI APIs, transformers, semantic search).
  • Strong understanding of CI/CD pipelines, GitHub workflows, and infrastructure-as-code.
  • Excellent problem-solving, communication, and collaboration skills.
  • Demonstrated ability to lead design efforts and deliver production-grade solutions.
  • Experience building or operating large-scale cloud services on Azure or another major cloud.
  • Familiarity with commerce, billing, or marketplace/storefront systems.
  • Experience with Kubernetes, event-driven architectures, or high-throughput data pipelines.
  • Experience building LLM-powered applications RAG pipelines, prompt engineering, agent frameworks (Semantic Kernel, LangChain), or fine-tuning with an eye for evaluation, latency, and cost.
  • Strong written communication: design docs, postmortems, customer-facing release notes.
  • Contributions to live-site / SRE practices: monitoring, alerting, incident response.

What the JD emphasized

  • responsible AI practices

Other signals

  • Integrate AI capabilities such as natural language processing, semantic search, and intelligent agents using frameworks like Azure OpenAI, Semantic Kernel, or equivalent.
  • Experience building LLM-powered applications RAG pipelines, prompt engineering, agent frameworks (Semantic Kernel, LangChain), or fine-tuning with an eye for evaluation, latency, and cost.