About

Rahul Ghosh is a passionate Data Engineer with expertise in scalable AI/ML, data pipeline, and enterprise AI product engineering. He completed his B-Tech in CSE with a specialization in Artificial Intelligence at NIIT University in 2020. Rahul is highly motivated and has a quick learning capability, which enables him to adapt rapidly to new environments. He enjoys learning and building highly scalable and deployable Machine Learning solutions. Rahul's areas of interest and expertise include data engineering, scalable data pipeline, designing data lake, data democratization, machine learning, deep learning, machine learning infrastructure engineering, machine learning lifecycle and data management, and RESTful web services. He has experience working with tools and technologies such as Python3+, C, Java, SQL, Tensorflow, Pandas, statsmodels, sk-learn, Mlflow, Ruby on Rails, Flask, Django, HTML/CSS, PostgreSQL, Git, Docker, Jenkins, Postman, Kubeflow, AWS, and Azure. Rahul has worked on industry projects such as the Leading Demand Sensing Platform (Thousense), which involved PAN India implementation of 18 months ahead Demand Forecasting at DEPOT, SKU, CHANNEL, and WEEK level for ~30k combinations, helping to reduce ~15% logistics cost using better sales forecasts at the most granular levels. He also led the ED-TECH Application (PlanetSpark), which leverages powerful technology to provide gamified learning to K8 learners in various fields like English Communication, Public Speaking, Robotics, Coding, Mental Maths, and STEM courses. Rahul has worked in industries such as Analytics (Personal Care, Beverages), Education Technology, and CPG. Aside from his work, Rahul loves teaching, traveling, music, cooking, and playing Cricket and Badminton. He is always open to discussing ideas that can solve various problems related to Education, Health, Agriculture, Business using Technology. Rahul's email is ghrahul2016@gmail.com, and his website is ghrahul.github.io. Currently working as a Data Engineer at Samya AI, Rahul has automated the end-to-end monthly data engineering process, which includes data collection, pre-processing, preparation, and ingestion using Kubeflow and automation modules written in python. He has created and integrated multiple SQL procedures (PL/pgSQL) with data ingestion pipeline for generating dynamic indexes and multiple materialized views of different data sources, improving the application performance by ~10.7x