Understanding Transformer Architectures in Modern NLP
A deep dive into the attention mechanism and how transformers revolutionized natural language processing, from BERT to GPT and beyond.
Hi, I'm
I'm a machine learning engineer specializing in AI, NLP, and computer vision. Currently, I'm focused on developing scalable ML infrastructure and conducting research in parameter-efficient fine-tuning of large language models.
Hello! I'm Shohanur, and I enjoy creating intelligent systems that solve real-world problems. My journey into AI began with curiosity about how machines could learn and has evolved into expertise in Natural Language Processing, Computer Vision, and Biomedical Image Processing.
Fast-forward to today, and I've had the privilege of working at companies ranging from early-stage startups to established enterprises. My work spans from academic research published in top-tier venues to production-ready systems deployed at scale, handling millions of requests daily.
When I'm not at the computer, I'm usually exploring the latest developments in AI, contributing to open-source projects, or sharing knowledge through technical writing and mentoring aspiring ML engineers.
Here are a few technologies I've been working with recently:
Developing and deploying MLOps-driven ML solutions to streamline business processes

Built scalable ML infrastructure and microservices. Managed CDC with MySQL using Debezium, Kafka, and Zookeeper. Delivered SmartRemarks and OCR-based TIN certificate validation system.
Developed large-scale distributed machine learning systems. Implemented MLOps principles including CI/CD for ML. Built diverse AI bots and designed APIs for ML/AI model integration.
Collaborated with international clients to design, train, and deploy ML solutions across computer vision, NLP, and predictive analytics. Delivered 30+ projects including classification, object detection, and speech recognition models.
A novel approach for prompt and prefix tuning in Large Language Models with synchronized label optimization. Published at ICLR 2024 Tiny Papers.
Leveraging Pre-trained CNNs for efficient feature extraction in rice leaf disease classification achieving high accuracy on agricultural datasets.
Universal Zero-Shot Natural Language Inference with Cross-Lingual Sentence Embeddings using contrastive learning. Published at EMNLP 2023.
Mohammad Majbah Uddin, Md. Shohanur Islam Sobuj
2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)
Nusrat Jahan Prottasha, Upama Roy Chowdhury, Shetu Mohanto, Tasfia Nuzhat, Abdullah As Sami, Md Shamol Ali, Md Shohanur Islam Sobuj, Hafijur Raman, Md Kowsher, Ozlem Ozmen Garibay
arXiv preprint
Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha, E. Alejandro Alanis, Ozlem Ozmen Garibay, Niloofar Yousefi
arXiv preprint
Nusrat Jahan Prottasha, Asif Mahmud, Md Shohanur Islam Sobuj, Prakash Bhat, Md Kowsher, Niloofar Yousefi, Ozlem Ozmen Garibay
Scientific Reports
Md. Shohanur Islam Sobuj, Md. Imran Hossen, Md. Foysal Mahmud, Mahbub Ul Islam Khan
2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)
A deep dive into the attention mechanism and how transformers revolutionized natural language processing, from BERT to GPT and beyond.
Lessons learned from deploying machine learning models at scale, including CI/CD pipelines, monitoring, and infrastructure considerations.
Exploring how deep learning techniques can be applied to optical coherence tomography images for diabetic retinopathy diagnosis.
How contrastive learning enables models to perform NLI tasks without task-specific training data, with practical implementations.
A practical guide to setting up event-driven ML pipelines using Apache Kafka, Docker containers, and microservices architecture.
From prompt tuning to parameter-efficient methods, explore different approaches to adapt LLMs for specific tasks and domains.
I'm always open to collaboration and new opportunities in AI research and development. Feel free to reach out if you'd like to discuss machine learning, research, or potential collaborations.
Or reach out directly at shohanur.ai@gmail.com
Prefer other ways to connect?