Microsoft Principal AI Infrastructure Developer 

United States, Washington 

649764077

As a, you will be pivotal in enhancing the quality of AI solutions, ensuring answer consistency, developing Answer Relevance, RAG (Retrieval-Augmented Generation) quality, and advancing our capabilities in map-reduce summarization, agent and action automation. You will get the opportunity to work in an agile environment, demonstrating technical expertise and a collaborative spirit. You will be a part of the Windows AI Team’s mission to integrate seamless, intelligent functionalities into Windows, driving unparalleled user experiences.

Required Qualifications

• Bachelor's Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
• 6+ years of experience in software development, with a solid track record of implementing and shipping high-quality services and designing and building scalable, reliable, and compliant software services.
• Experience in AI infrastructure development, with experience in large-scale AI projects.
• Proficiency in technologies including: AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn), RAG systems and map-reduce frameworks and agile development methodologies and tools.

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Find additional benefits and pay information here:

Microsoft will accept applications for the role until August 16, 2024.

• Design and Implement AI-Driven Services: Develop and optimize AI-driven services, ensuring they meet both technical standards and user expectations.
• AI Quality Enhancement: Implement and refine processes to ensure the highest quality of AI outputs, focusing on accuracy, reliability, and performance.
• Metrics and Customer Insights: Translate telemetry and logging data into actionable insights to refine and enhance service offerings.
• Operational Issue Management: Proactively handle operational issues as they arise, providing timely resolutions and maintaining service integrity while applying those learnings to prevent future issues.
• Continuous Improvement: Stay updated with the latest advancements in AI and machine learning, incorporating cutting-edge techniques and technologies into ongoing projects.

• Other

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