Radhika Kanubaddhi has made a significant impact on the world of artificial intelligence (AI) and machine learning (ML) working for some of the largest technology companies. With a strong background in IT, Radhika has delivered innovative solutions that have transformed the way businesses operate. In this interview, she talks about her work at Epsilon, Microsoft, and Amazon, and shares her insights on AI and ML advancements in big tech.
Could you tell us about your early work at Epsilon and how you contributed to the advancement of AI and ML?
At Epsilon, I led a team that worked on the development State-of-the-art ML recommendation engines. One of our most notable accomplishments was implementing a real-time recommendation engine for an airline, which resulted in a $214 million revenue increase in 30 days. Additionally, I piloted an email marketing campaign for a retail client with a 27% increase in orders over eight weeks. I also developed an email recommendation engine, contributing to a 31% increase in customer ticket purchases. My work there focused on connecting technical solutions with measurable business results.
How has your work at Microsoft evolved and what key projects have you worked on there?
At Microsoft, I focused on building and deploying AI solutions, particularly enterprise chatbots. One of my key projects was developing a cloud-native chatbot solution for a hospitality client, using Azure QnA Maker and Azure LUIS. This project generated $1 million in annual revenue by helping the client adopt cloud solutions. My job at Microsoft was to understand our customers’ needs and guide them in implementing AI solutions that would improve their operations. I had the chance to work on natural language processing (NLP) technologies, which have paved the way for more intuitive customer interactions.
What challenges did you encounter while developing these AI solutions at Microsoft?
Developing AI solutions often comes with challenges, especially when working on large-scale enterprise systems. One of the challenges was ensuring that AI technologies were scalable and adaptable to meet changing customer needs. Understanding the fundamentals of AI and machine learning helped me overcome these complexities. I also worked closely with client executives to ensure our solutions met their strategic objectives.
What type of work have you focused on at Amazon and how does it relate to AI and database technology?
At Amazon, I developed database technology which supports AI applications used on Amazon’s platforms. One of my most significant achievements was the development of a high-efficiency database capable of operating with a single millisecond latency. It is a critical component for AI and machine learning applications because they require real-time data access and processing capabilities. My work at Amazon focused on optimizing systems for speed and reliability to ensure AI applications run optimally.
As a female engineer, have you encountered any challenges and how did you work to overcome them?
Yes, there have been challenges as a woman in the field of engineering, a sector still dominated by men. However, I was fortunate to rely on a solid theoretical foundation in computer science and problem solving to overcome these challenges. I also spend time teaching engineering and computer science to high school girls to encourage more young women to explore STEM fields. Promoting diversity and inclusion in the tech industry is important, and I try to make a positive impact in this area.
What excites you most about the future of AI and what are your aspirations in this area?
I’m excited about the advancements in generative AI. AI has immense potential to revolutionize industries and create more intuitive and efficient solutions. Looking ahead, I hope to continue working on cutting-edge AI technologies and contribute to the development of solutions that will benefit businesses and society as a whole. I also want to continue to mentor and encourage more women to enter the AI and technology fields.
