Radhika Kanubaddhi

Radhika Kanubaddhi has over 12 years of experience as a software developer, architect, and solutions provider. She is currently working as AI specialist at Amazon Web Services. Previously, she worked as Cloud Solutions Architect at Microsoft. Before that, she developed and deployed machine learning models to improve revenues, profits, increase conversions for clients from various industries such as airlines, banks, pharma, retail, and hospitality. She is an expert in assembling the right set of services to solve client needs.

Radhika has worked with almost all technical innovations and services in the last decade - including Internet of Things, cloud application development, ML models, AI apps, Azure, AWS etc.

Some of her accomplishments include:

- Led Amazon Keyspaces databases that supports more than 100,000 read requests per second.

- Led three ML recommendation engine POCs converting 2 out of 3 clients resulting in $1.17M annual revenues.

- Implemented real-time recommendation engine for an airline client resulting in $214M increase in 30-day revenue.

- Developed ‘Backup as a Service’ to clone and encrypt data from millions of Internet of Things (IoT) devices

Radhika also creates content to share her knowledge and enthusiasm for the latest technical innovations.

Radhika has Master’s in Computer Science and Bachelor’s in Information Technology.

Amazon Nov 2022 – Present (18 months)

Led the team building an important feature called “frozen collections” in Amazon Keyspaces (for Apache Cassandra) which gets 100k requests per second and supports single millisecond latency. This feature reduces the cost and ingress and egress latency because of the way it allows data to be stored and managed. Development spanned across multiple quarters and I led four engineers to develop.

https://aws.amazon.com/blogs/database/announcing-frozen-collections-in-amazon-keyspaces/

https://aws.amazon.com/about-aws/whats-new/2023/03/amazon-keyspace-apache-cassandra-client-side-timestamps/

Microsoft Apr 2021 – Nov 2022 (20 months)

Developed numerous proof of concepts (POCs) for client teams in data and AI services. Specifically, I crafted a POC and designed an enterprise-grade, cloud-native chatbot solution for a hospitality client, leveraging Azure QnA Maker and Azure LUIS. This was prior to the advent of ChatGPT and large language models (LLMs). Engaged in direct discussions with client executives to understand their strategic roadmap and facilitate their cloud adoption journey, optimizing infrastructure costs along the way. Notably, some of these clients' leadership teams included CTOs, reflecting my work with small and medium businesses.

https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/introducing-azure-cognitive-service-for-language/ba-p/2910735

EPSILON Sep 2018 – Apr 2021 (31 months)

Led a team developing real-time and batch based ML recommendation engines for leading airlines, hospitality, retail and financial clients. Used H2O, pytorch, scikit learn, keras in scikit learn for building deep learning models. Productionized the ML models, built end to end ML pipelines. Communicated model performance metrics with senior leadership in the client teams including talking to C-level leaders.

This is significant because clients revenues have increased by more than 20% in the case of Marriott and thereby increasing overall ML business for Epsilon.

https://www.epsilon.com/us/client-success/case-studies/marriott-international [My project published as case study]

https://www.epsilon.com/us/insights/resources/epsilons-practical-application-of-machine-learning-video [Presenting our insights to clients]

Scholar Profile: https://scholar.google.com/citations?user=hAj8m4gAAAAJ&hl=en