Advancing Microsoft's mission to empower every individual and organization on the planet to achieve more through Agentic AI by developing and integrating cutting-edge AI technologies into Microsoft products and services.
Requirements
- 1+ year(s) Experience in developing and deploying live production systems.
- Building RAGs and fine-tuning speech language models Whisper, wav2vec2 etc.
- Understanding of SLU, ASR, TTS, NLP, NLU domain.
- Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow).
- Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.
- Experience in working with Generative AI models and ML stacks.
- 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
- 3 days / week in-office
- Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
- This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
- Demonstrate a growth mindset and customer empathy.
- Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
