Senior Applied AI Engineer

Role Summary

As a Senior Applied AI Engineer for The Customer Service Applications Team, you will play a pivotal role in advancing Microsoft's mission to empower every individual and organization on the planet to achieve more. You will contribute to the development and integration of cutting-edge AI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful.

You will collaborate acrossproduct,researchand engineering teams to bring innovative solutions to life, applying yourexpertisein machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experiences.

AIMission and Impact

We are in an era of unprecedented innovation and openness. As Microsoft continues tolead inAI, we are seeking individuals to help tackle some of the most exciting and meaningful challenges in the field. Our vision is to builda truly open architecture platform that enables users to summon tailored AI agents to drive real-world outcomes.

We are looking for an(insert role/ external title)to join our team

This rolewill combineAI knowledge withapplied scienceexpertise,anddemonstrate a growth mindsetandcustomer empathy. Join us in shaping the future of AI agents.

Microsoft's mission is to empower every person and every organization on the planet to achieve more. Asemployeeswe come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

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-tunefoundation models using domain-specificdatasets. -Evaluate model behavior on relevance, bias, hallucination, and response qualityvia 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 conferencepresentations. -Translate research into production-ready solutionsand measure their impact through A/B testing and telemetrythat addresscustomer needs.
• Ability to use data toidentifygaps in AI quality, uncoverinsightsand implementPoCsto show proof of concepts.
• Proven programmingexpertise(e.g., in Python orleveragingAI-first IDEs and SWE agents), with a strong record of building reliable, well-documented research code that drives rapid experimentation, scalable evaluation, and efficient deployment from prototype to production in applied AI research.

Leveraging Researchin real-world problems

• Demonstratedeepexpertisein 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-artresearch 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,Privacyand Security

Apply a deep understanding of fairness and bias in AI by proactivelyidentifyingand mitigating ethical and security risks-includingXPIA(Cross-Prompt Injection Attack)unfairness, bias, and privacy concerns-to ensureequitableand responsible outcomes.

• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
• Contribute to internal ethics and privacy policies andensure responsible AIpracticethroughoutAIdevelopment cyclefrom data collectionto model development, deployment, and monitoring.

Specialty Responsibilities

• Design, develop, and integrate generative AI solutions usingfoundation models and more.
• Deep understanding ofsmall 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,identifyingoptimalfeaturesandaddressingdata 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 andalerting,andadapt to changes in data streams.