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Microsoft is seeking to develop and integrate cutting-edge AI technologies into its products and services, aiming to create a truly open architecture platform that enables users to summon tailored AI agents for real-world outcomes. The goal is to empower every person and organization to achieve more through AI.

Requirements

• 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)
• 1+ year of experience developing and deploying live production systems
• Experience across the product lifecycle from ideation to shipping
• 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

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

• Ability to pass Microsoft Cloud Background Check upon hire and every two years thereafter.
• Embody our culture and values.
• Demonstrate a growth mindset and customer empathy.
• Collaborate across product, research and engineering teams.
• Build collaborative relationships with product and business groups to deliver AI-driven impact.