Senior Software Engineer on the Azure Hardware Health Service, part of Microsoft Cloud Hardware, Infrastructure Engineering (SCHIE). The team designs, implements, and operates global, scalable cloud services to monitor hardware health and predict anomalies and impending failures. Azure Hardware Health Service focuses on delivering low cost of ownership and a trusted experience for customers and partners globally. Responsibilities include developing and operating large-scale, low-latency, high-throughput systems; evaluating AI technologies (such as LLMs, SLMs, embeddings) and architectures (including orchestration patterns and RAG) for firmware and hardware improvements; implementing AI platform enhancements like fine-tuning or training custom ML models; driving continuous improvement via root cause analysis; contributing to data analysis and feedback for product engineering decisions as a designated responsible individual under SLA; participating in live service operations and telemetry integration to gather insights on performance, reliability, and safety; identifying dependencies and documenting architecture for product features; producing code to test hypotheses and assist with technical validation; collaborating on QA plans, augmenting test cases, and integrating automation into testing while considering security and compliance; ensuring compliance with security, privacy, safety, and accessibility standards; leveraging developer tools for coding and debugging; contributing to automation in production and deployment; and defining and measuring the success of analytics an

Senior Software Engineer on the Azure Hardware Health Service, part of Microsoft Cloud Hardware, Infrastructure Engineering (SCHIE). The team designs, implements, and operates global, scalable cloud services to monitor hardware health and predict anomalies and impending failures. Azure Hardware Health Service focuses on delivering low cost of ownership and a trusted experience for customers and partners globally. Responsibilities include developing and operating large-scale, low-latency, high-throughput systems; evaluating AI technologies (such as LLMs, SLMs, embeddings) and architectures (including orchestration patterns and RAG) for firmware and hardware improvements; implementing AI platform enhancements like fine-tuning or training custom ML models; driving continuous improvement via root cause analysis; contributing to data analysis and feedback for product engineering decisions as a designated responsible individual under SLA; participating in live service operations and telemetry integration to gather insights on performance, reliability, and safety; identifying dependencies and documenting architecture for product features; producing code to test hypotheses and assist with technical validation; collaborating on QA plans, augmenting test cases, and integrating automation into testing while considering security and compliance; ensuring compliance with security, privacy, safety, and accessibility standards; leveraging developer tools for coding and debugging; contributing to automation in production and deployment; and defining and measuring the success of analytics an