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Microsoft Principal AI Infrastructure Developer 
United States, Washington 
649764077

13.08.2024


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.

Responsibilities
  • 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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