Alexandra Savelieva is a Principal Applied AI Engineer Azure Data group, working in the Applied AI team that focuses heavily on Large Language Model (LLM) applications grounded in enterprise data. It is a small team of hands-on engineers who work with select customers in the context of short & in-depth engagements laser-focused on developing an MVP of a customer-driven use cases and offer guidance by sharing patterns and samples with Customers, Cloud Solutions Architects, specialists, GBBs, etc. As part of this job, Alexandra works on real world LLM scenarios to improve Microsoft products and documentation, maximize customer impact and empower other engineers (Customers, ISVs, Internal Microsoft teams) by sharing samples, patterns, and best practices.

Throughout her professional journey in Microsoft, she’s been working with data in various capacities: starting from SRE in Bing Ads AuditTrail team, then as a Software engineer / Security and Reliability champion in a distributed application platform in Information Platform group, then as a Data scientist, Engineer and Insights team lead in Azure Frontdoor, then as a technical lead of collaborations between MAIDAP, Azure IoT, and Microsoft Dynamics 365 on development of innovative AI/ML solutions, and educational content developer for semantic data science part of Microsoft Fabric. Alexandra is a co-lead of the AI/ML connected community in Microsoft (currently over 20k members) driving efforts to help people across the company exchange of expertise and create synergy between AI-powered projects. From 2022 to 2023 Alexandra was a part-time lecturer at UC Berkeley, teaching “Deep Learning in the Cloud and at the Edge”.

Alexandra is regularly presenting and volunteering at conferences, such as KDD, Neurips, and MLADS. Alexandra has a PhD in Computer Science (2011) and Master of Information and Data Science (2021).