Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) is the team behind Microsoft’s expanding Cloud Infrastructure and responsible for powering Microsoft’s “Intelligent Cloud” mission. SCHIE delivers the core infrastructure and foundational technologies for Microsoft's over 200 online businesses including Bing, MSN, Office 365, Xbox Live, Teams, OneDrive, and the Microsoft Azure platform globally with our server and data center infrastructure, security and compliance, operations, globalization, and manageability solutions. Our focus is on smart growth, high efficiency, and delivering a trusted experience to customers and partners worldwide and we are looking for passionate engineers to help achieve that mission.

As Microsoft's cloud business continues to grow the ability to deploy new offerings and hardware infrastructure on time, in high volume with high quality and lowest cost is of paramount importance. To achieve this goal, the SW/FW Centre of Excellence team is instrumental in defining and delivering operational measures of success for hardware manufacturing, improving the planning process, quality, delivery, scale and sustainability related to Microsoft cloud hardware. We are looking for seasoned engineers with a dedicated passion for customer focused solutions, insight and industry knowledge to envision and implement future technical solutions that will manage and optimize the Cloud infrastructure.

• Bachelor’s degree in Computer Science, Computer Engineering, or a related field.

• 7+ years of industry experience in AI/ML engineering using platforms and languages/frameworks such as

• Proven experience in designing, building, and deploying AI agents across the autonomy spectrum—from retrieval-based to task-oriented and autonomous agents.

• Strong background in developing web applications and services that integrate AI/ML models for business insights and automation.

• Hands-on experience with large language models (LLMs), including training, fine-tuning, and inference optimization for multi-billion parameter models.

• Familiarity with the full ML lifecycle: data engineering, model training, evaluation, deployment, and monitoring.

• Understanding of embedded systems, firmware development, OS concepts is a strong plus.

• Determines the technique needed and develops analytic models to understand complex business issues and provide data-driven insights by integrating statistical inference, machine learning modeling, AI and/or other advanced analytical methods to manage, classify and analyze complex data from a variety of sources.
• Conduct A/B analysis, create and validate metrics, develop ML pipeline and modeling algorithm in Information Retrieval and Machine Learning.
  Perform data analysis using a variety of analytical tools (Python, KQL, MLStudio, Synapse, Power BI, Fabric etc), and interpret results with actionable recommendations.
• Define & measure the success/impact of requested analytics & reporting features via quantitative measures.
• Leverage and advance Deep Learning, Reinforcement Learning, Causal Inference, and other techniques to solve complex problems.
• Take an active role and partner with internal peer teams and external partners to ensure highly available, fully secure, accurate and actionable results based on hardware health signals, policies, and predictive analytics.
• Partner with stakeholders across the team to identify opportunities to build new AI-based solutions to improve engineering efficiency across development, validation, monitoring, and live support