Microsoft Senior AI Applied Engineer 

Taiwan, Taoyuan City 

221919847

You will collaborate across product,

This role will combine AI knowledge with applied scienceexpertise, and

Required Qualifications

• Bachelor’s degree in computer science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND experience in AI/ML, predictive analytics, or research- OR Master’s degree- OR PhD- OR equivalent experience

• Experience with generative AI OR LLM/ML algorithms

Other Requirements:

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

• requiredto pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Experience withMLOpsWorkflows, including CI/CD, monitoring, and retraining pipelines.

• Familiarity with modernLLMOpsframeworks (e.g.,LangChain,PromptFlow

• Experience developing and deploying live production systemsone or more of the following: C#, Java, React/Angular, TypeScript.

• Experience with design and implementation of enterprise-scale services

• Experience publishing in peer-reviewed venues or filing patents

• Experience presenting at conferences or industry events

• Experience conducting research in academic or industry settings

• Experience working with Generative AI models and ML stacks

• Experience across the product lifecycle from ideation to shipping

Bringing the State of the Art to Products

• Build collaborative relationships with product and business groups to deliver AI-driven impact
• Research and implementstate-of-the-artusing foundation models, prompt engineering, RAG, graphs, multi-agent architectures, as well as classical machine learning techniques.
• Fine-tune foundation models using domain-specific datasets. - Evaluate model behavior on relevance, bias, hallucination, and response quality via offline evaluations, shadow experiments, online experiments, and ROI analysis.
• Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, supportMLOps/AIOps.Contribute to papers, patents, and conference presentations. - Translate research into production-ready solutions and measure their impact through A/B testing and telemetry that address customer needs.
• Ability to use data toidentifygaps in AI quality, uncover insights and implementPoCsto show proof of concepts.

Leveraging Research in real-world problems

• Demonstrate deepexpertisein AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models) to translatecutting-edgeresearch into practical, real-world solutions that drive product innovation and business impact.
• Share insights on industry trends and applied technologies with engineering and product teams.
• Formulate strategic plans that integratestate-of-the-art

Documentation

• Maintain clear documentation of experiments, results, and methodologies.
• Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing

Apply a deep understanding of fairness and bias in AI by proactivelyand responsible outcomes.

• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
• Contribute to internal ethics and privacy policies and ensure responsible AI practice throughout AI development cycle from data collection to model development, deployment, and monitoring.

Specialty Responsibilities

• Design, develop, and integrate generative AI solutions using foundation models and more.
• Deep understanding of small and large language models architecture, Deep learning, fine tuning techniques, multi-agent architectures, classicalML,  andoptimization techniques to adapt out-of-the-box solutions toparticular businessproblems
• Prepare and analyze data for machine learning,identifyingoptimalfeatures and addressing data gaps.
• Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks andstate-of-the-artmodels, open-source libraries, statistical tools, and rigorous metrics
• Address scalability and performance issues using large-scale computing frameworks.
• Monitor model behavior, guide product monitoring and alerting, and adapt to changes in data streams.