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Nederlands
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Rokibul
@rokibulislam961
AI,ML Engineer
Bangladesh
Engels, Hindi
Over mij
AI/ML Engineer specializing in computer vision, LLM/VLM fine-tuning, and RAG systems. Experience fine-tuning OpenVLA, SmolVLA, and BanglishBERT; building CNN-based segmentation/detection pipelines; and developing agentic AI workflows with LangGraph and MCP. Comfortable across the full pipeline — data processing, training, evaluation, deployment (FastAPI/Django). Native Bangla speaker, strong edge in code-mixed NLP. CSE graduate (AI/ML concentration), researching robotic scene understanding. Reliable communication, clean code, and real problem-solving.
Skills
Rokibul
offline •
Gemiddelde reactietijd: 1 uur
Bekijk mijn diensten
Implementatie en uitrol van AI
I will build computer vision solutions with yolo, CNN, and opencv
Portfolio
Werkervaring
Research Assistant
ArxEd
Dec 2025 - May 2026 • 5 mos
Research Assistant — Computer Vision / Deep Learning Optimization Designed and led a benchmarking framework to evaluate the impact of different optimization algorithms (including MTAdamV2 and MTMuon) on convolutional neural network performance for image classification tasks. Implemented and trained CNN architectures — including MobileNetV3-Large and ResNet-18 — under controlled experimental conditions to isolate the effect of optimizer choice on convergence speed, generalization, and final accuracy. Built a reproducible experimentation pipeline in Python/PyTorch to systematically compare optimizer variants across multiple model backbones, enabling fair, apples-to-apples performance evaluation. Analyzed training dynamics (loss curves, convergence behavior, stability) across optimizers to identify trade-offs relevant to real-world model selection under compute constraints. Documented methodology, results, and findings in a structured repository, contributing to the broader body of applied research on optimizer selection for CNN-based computer vision tasks. Applied skills in deep learning architecture design, hyperparameter tuning, and empirical evaluation — foundational to later work fine-tuning larger vision-language-action models (SmolVLA, OpenVLA) for robotics applications.
