Tina Lekshmi Kanth is a Software Engineer II at Microsoft in Charlotte, NC, specializing in data engineering, machine learning, and big data analytics.

At Microsoft, Tina has architected real-time financial event processing systems that achieved 100X processing speed improvements for near real-time revenue reporting using Azure-based Spark Streaming and Kubernetes microservices. She developed AI automation solutions that improved SRE efficiency by 40% and led the design and implementation of business continuity and disaster recovery systems for financial data pipelines.

Tina holds a Master's degree in Data Science (Computer Science and Mathematics) from Illinois Institute of Technology and a Bachelor's degree in Electrical & Electronics Engineering from College of Engineering Perumon, Kerala, India. Her technical expertise includes Scala, Python, SQL, C#, Apache Spark, Azure, Kubernetes, and various machine learning frameworks.

Previously, she worked as a Data Engineer at Red Ventures, where she designed scalable ETL pipelines that contributed to a 20% revenue increase through improved marketing analytics. She also served as a Data Scientist at Wolters Kluwer Legal, developing NLP-based search optimization systems for legal content, and worked as a Marketing Data Intern at Maestro Health, building predictive lead scoring models with 78% accuracy.

Her earlier experience includes roles as a Warranty Data Analyst at Qfab in Qatar and a Data Warehousing Programmer Analyst at Cognizant Technology Solutions, where she achieved 30% cost reductions through data-driven process improvements.

Tina led the creation of the "Responsible AI in Microsoft Sovereign Clouds" white paper and has developed various projects, including real-time traffic data processing systems, multilabel classification models achieving 89% accuracy, and CNN-based image recognition systems with 91.7% accuracy.
 

Presentations

23x

Secure Prompt Engineering at Scale

Millions of financial events. Zero room for error. Learn how template-driven prompt engineering turns noisy cloud transaction streams into explainable, policy-safe automation, faster incident triage, smarter anomaly detection, instant schema-drift recovery, and audit-ready trails.

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