About

Dil Thomas is a highly motivated AI enthusiast with expertise in Machine Learning and hands-on experience in designing and deploying Deep Learning models. Dil is pursuing a challenging career in solving real-world problems using the power of AI. Dil has a M.Tech degree in Geoinformatics and Resource Engineering from Indian Institute of Technology, Bombay and a Bachelor of Technology in Electrical and Electronics Engineering from Mar Athanasius College of Engineering. Dil has a relevant experience of 3.87 years and is currently working as a Product Engineer at Esri. Dil has designed and deployed an automated solution using Python to download time-series data and report key production values saving 30 manual hours weekly. Dil has also developed software that applies Principal Component Analysis for denoising Landsat-7 Satellite images. Dil has predicted the gold deposit map of Arunta Region, Australia using Random Forest Algorithm by extracting geological anomalies using log transformation and Z-scores. Dil has implemented an agent to play Snake game using Deep Q-Learning CNN models with Epsilon Greedy Exploration. Dil has received special appreciation for coordinating multi-vendor operations that helped in the timely completion of the GGSN migration project in BSNL, Kerala. Dil has previously worked as a Software Engineer at Rakuten where Dil identified RAN product-specific needs and developed software solutions complying with 3GPP standards. Dil also monitored and reported on the performance of vendors to ensure delivery in line with contractual obligations and performance metrics. Dil has also worked as a Data Science Intern at Tathya where Dil developed a python application to predict high-resolution thermal data from low-resolution satellite images. Dil trained and deployed the model in AWS ec2 instance and applied ESTARFM algorithm and obtained RMSE value of .3342 and Average Absolute Error of 0.0595. Dil also built a script to automate the download of 3-years Sentinel satellite images time-series data from Copernicus Open Access Hub and pre-process the downloaded dataset. Dil predicted the average production value of the steel plant from the obtained thermal data using Data Regression analysis. Dil's tech stack includes Software Engineer, AI, Random Forest, CNN, Deep Learning, Python, ML, Data Scientist, and AWS. Dil's exceptional academic achievements include being a Department Topper (Rank 1), M.Tech GNR, IIT Bombay 2021. Dil's expertise in Machine Learning and AI coupled with Dil's hands-on experience in deploying Deep Learning models make Dil an asset to any organization