About

Karthik Vullam is an experienced Machine Learning/Deep Learning Engineer with a strong background in Python programming and Cloud services such as AWS. He holds a Master's Degree in Electrical Engineering from the Indian Institute of Technology, Kharagpur. With over 5 years of relevant experience, Karthik has a demonstrated history of working in the information technology and services industry. Currently working as a Machine Learning Software Engineer at Cargill, Karthik has developed a deep-learning image classification model for crop disease detection, using TensorFlow and Keras in Maize, Paddy, and Arecanut. He has also integrated this model into a user-friendly mobile application, allowing farmers to scan images of their crops and receive disease information and recommendations on curing diseases. Karthik has conducted extensive data pre-processing and augmentation to enhance the performance of the model and collaborated with agronomists to integrate expert knowledge into the recommendation system, ensuring that the results were both accurate and actionable. He has deployed models using FastAPI to provide inference in real-time, which has had a positive impact on 75K farmers. Prior to joining Cargill, Karthik worked at Accenture as an Application Development Senior Analyst and Application Development Analyst. At Accenture, he built a POC of a fashion recommendation system using Attn GAN which generates images of the fashion apparel and these are used for the refined search of the user query together with the search term. He also developed an image classification model of female celebrities using transfer learning (ResNet 50 & Mobile Net weights) and deployed it to the Google chatbot, where the predicted class results were displayed as their respective celebrity web page giving more information about them. Karthik is proficient in computer vision with academic as well as industry-level projects and has a strong engineering background. He is actively looking for full-time positions as a Data Scientist or Machine Learning Engineer. His tech stack includes ML, Software Engineering, AWS, Computer Vision, Recommendation System, Python, Search, Deep Learning, Keras, Research Scientist, NLP, Mobile, GAN, Tensorflow, Integration, ResNet, Object detection, Object Detection, CI/CD, Flask, and Web.