Advancing Microsoft's mission to empower every individual and organization on the planet to achieve more by developing and integrating cutting-edge AI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful. The vision is to build a truly open architecture platform that enables users to summon tailored AI agents to drive real-world outcomes.
Requirements
- 1+ years of experience with Generative AI OR Large Language Model (LLM)/Machine Learning algorithms.
- 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
- 1+ years of experience working with Generative AI models and ML stacks.
- 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-Sharp, Java, React/Angular, TypeScript.
- 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
Responsibilities
- 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.
- 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.
- Design, develop, and integrate generative AI solutions using foundation models and more.
Other
- Build collaborative relationships with product and business groups to deliver AI-driven impact
- Contribute to papers, patents, and conference presentations.
- 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.
