Led the integration of an LLM-based AI chatbot using Twilio that services travel-specific queries by the travelers/travel agents. It was a high-impact project that helped in decrease of the load on the human support agents.

Developed an AI based pipeline that detects the sentiment of the customers when interacting with the support agents and provides AI suggestions using LLM to the support agents that can be sent to the customers. Received the ‘AI Demo Winner’ award for it from the company

Responsible for development and enhancement of the dashboards that display support tickets corresponding to customers’ queries as per the product requirements

Set up and performed large scale experiments to identify bottlenecks in performance and improved the performance of the system. Added rich telemetry and monitoring to raise alert in case of issues

Responsible for designing new features for the virtual networks with enhanced reliability and

Developed supervised machine learning/deep learning models (including training, fine-tuning and testing) for extraction of required information from highly diverse, form-like documents

Improved the accuracy metrics for multiple required fields with some of them significantly higher (by around 25% to 35%) than the acceptable minimum level

Spearheaded the project titled ‘Smart Refrigerator’. Developed machine learning models to detect the type of food item and whether that food item is being taken out or put inside the refrigerator

Built an end-to-end system that collects images using multiple camera on opening the refrigerator

Created a rich dataset of over 75000 images related to human-refrigerator interaction by collecting images from 25 human volunteers

Detected hands in the images using Tensorflow object detection API in Python by applying transfer learning on the SSD model pre-trained on Egohands dataset