Summary
Labib Chowdhury is a Machine Learning Engineer with nine years of experience and over six years focused on ML/DL, known for taking products from 0→1 and scaling production systems that handle millions of posts and high traffic. He has led cross-functional teams to deliver NLP and CV solutions—improving Bengali NER, face anti-spoofing, OCR and large-scale face verification—while contributing to revenue growth and reducing release and outage times through MLOps and microservices. His work spans research and practice, with publications at ACM KDD, NAACL and EMNLP and productionized pipelines on Databricks, MLflow and DVC. Skilled in Python, C++, Go, PyTorch and Hugging Face, he blends deep metric and representation learning expertise with hands-on engineering to deploy robust, multilingual ML systems. Labib’s background in biometrics and long-tailed classification gives him an edge in building resilient models for real-world, low-resource language settings.
9 years of coding experience
6 years of employment as a software developer
Bachelor’s Degree, Computer Science & Engineering, Bachelor’s Degree, Computer Science & Engineering at North South University
Higher Secondary School Certificate (HSC), Science, Higher Secondary School Certificate (HSC), Science at St. Joseph Higher Secondary School
English, Bengali