Microsoft is seeking to tackle exciting and meaningful challenges in the field of AI by developing innovative solutions that enhance Microsoft products and services, influence product strategy, and create meaningful customer experiences through the Agent 365 team.
Requirements
- Demonstrate deep expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models.
- 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.
- Address scalability and performance issues using large-scale computing frameworks.
- 1+ years of experience with generative AI or LLM/ML algorithms.
- Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.
- Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow).
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.
- 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.
- 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.
- Monitor model behavior, guide product monitoring and alerting, and adapt to changes in data streams.
Other
- Build collaborative relationships with product and business groups to deliver AI-driven impact.
- Contribute to papers, patents, and conference presentations.
- 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.
- 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.
