Senior AI Applied Engineer

Bringing the State of the Art to Products  

• Build collaborative relationships with product and business groups to deliver AI-driven impact  
• Research and implement state-of-the-art using 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, support MLOps/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 to identify gaps in AI quality, uncover insights and implement PoCs to show proof of concepts.  
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Leveraging Research in real-world problems  

• Demonstrate deep expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models) to translate cutting-edge research 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 integrate state-of-the-art research to meet business goals.  

Documentation  

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

Ethics, Privacy and Security  

Apply a deep understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks—including XPIA (Cross-Prompt Injection Attack) unfairness, bias, and privacy concerns—to ensure equitable and 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, classical ML,  and optimization techniques to adapt out-of-the-box solutions to particular business problems  
• Prepare and analyze data for machine learning, identifying optimal features and addressing data gaps.  
• Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks and state-of-the-art models, 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.  

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:  

• 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.  

Preferred Qualifications:  

• Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.  

• Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow)  

• Experience developing and deploying live production systems one 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  

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.  

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work. 

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