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I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.

Presently pursuing my M.Sc. degree in Biomedical Engineering at Bangladesh University of Engineering and Technology, Bangladesh. I am currently working on biomedical signal processing and deep learning-based health informatics projects as an active member of the m-health lab. My other research interests include biomedical simulations, biomedical Instrumentation, and edge device optimization. I am pretty much proficient in python and Matlab programming.

With a strong background in machine learning, computer vision, and natural language processing, I have consistently delivered real-world AI solutions across diverse industries. My professional journey spans multiple roles where I have developed and deployed intelligent systems for image and language understanding, focusing on accuracy, performance, and scalability. Currently working as a freelancer, I have built intelligent data extraction pipelines for financial applications by combining advanced computer vision and NLP methods to extract information from bank cheques with high precision. Additionally, I worked on a complex chatbot project addressing the limitations of large language models (LLMs), such as context window constraints. I implemented optimization strategies like history truncation, prompt summarization, and prompt caching, which significantly improved coherence, processing speed, and memory efficiency in generated responses. As a Data Science Fellow at Fellowship.AI, I developed a robust evaluation pipeline to measure confidence levels in LLM outputs using datasets like LiveBench and MMLU. I applied strong evaluation metrics such as Expected Calibration Error (ECE) and BERTScore and utilized stronger LLMs for benchmarking, providing valuable insights into model calibration and reliability. Previously, as a Computer Vision Engineer at Hello Llama, I played a pivotal role in building IoT-enabled safety solutions. I developed end-to-end ML pipelines and Android applications integrating BLE and real-time video streaming, designed to work with radar warnings and dashcam systems. My work focused on real-time object detection of urban infrastructure and safety violations, deploying models on NVIDIA Jetson Nano for low-latency, edge-based inference. I also created a custom helmet detection system with facial focus and chin-strap detection and used sensor mat data to recognize multiple riders on scooters. My responsibilities included complete development cycles—from data collection to annotation, model training, validation on unseen data, and deployment—with comprehensive testing to ensure robustness in production environments. Earlier in my career as a Machine Learning Engineer at Expert Consortium Ltd., I worked on automatic face recognition and liveness detection systems. I designed pipelines capable of distinguishing live individuals from photographs and automatically created datasets for unknown individuals by organizing webcam feeds, labeling them, and training recognition models using LBP. I also built a driver activity recognition system for behavioral monitoring and an object tracking tool that captured snapshots when specific spatial triggers were activated. I utilized GPU acceleration and RabbitMQ to ensure high performance and seamless message passing during training and inference. Throughout my experience, I have demonstrated a unique ability to bridge the gap between computer vision and natural language processing. I have built robust pipelines, optimized LLM performance, engineered edge-deployable vision systems, and created intelligent, real-time applications in safety, finance, and mobility. My strengths lie in developing production-ready ML systems, optimizing performance in resource-constrained environments, and delivering intelligent solutions that scale effectively and meet real-world demands. I have direct Experience working on following topics: C++ Python Scikit-learn Tensorflow Keras OpenCv Embedded device Aws Azure Flutter

I am an expert AI/ML Engineer with hands-on expertise in Machine learning, Deep Learning, Computer Vision, NLP, Generative AI, and LLMs. I leverage advanced techniques to deliver innovative and impactful AI solutions for your needs. Areas of expertise: ✅ Machine Learning (Data Analysis and Visualization, Classification, Regression models, Predicting with Supervised, Unsupervised Techniques) ✅ Deep Learning (Deep Neural Networks, Transfer Learning, fine-tuning models, XAI, optimization, exploring models) ✅ Computer Vision and Image Processing (Object detection/segmentation/tracking, Image/Video classification, Image generation, OCR) ✅ NLP (Text classification, Topic modeling, NER, Semantic similarity, Text generation, Question Answering) ✅ Generative AI (Fine-tuning LLMs, RAG, Prompt Engineering, embeddings, vector database, Chatbots and AI Assistants) ✅ Audio Data Analysis (Audio classification, Sound segmentation, TTS, STT) Tech Stack: ✅ ML/DL tools: TensorFlow, Keras, PyTorch, Scikit-Learn, XGBoost, LightGBM, CatBoost ✅ NLP: LangChain, OpenAI(GPT-4, Embeddings, ChatGPT), Local LLMs, Huggingface, Gemini, NLTK, Textblob ✅ Computer Vision: Ultralytics, OpenCV, Pillow, Scikit Image, YOLO, Detectron2, OpenPose ✅ Data Analysis/Visualization Tools: Pandas, NumPy, Matplotlib, Seaborn, Plotly, Altair ✅ Cloud services: AWS, Google Cloud, Azure ✅ Project Management and Version Control: Git, GitHub ✅ MLOps: MLFlow, Docker, Github actions, Airflow, DVC ✅ Deployment: StreamLit, Flask, FastAPI ✅ Languages: Python My commitment to excellence and continuous learning ensures that I stay at the forefront of AI/ML advancements. Let’s connect to explore how I can contribute to your AI/ML projects and help you achieve your business goals with cutting-edge solutions.

⭐⭐⭐⭐⭐ Turn your ideas into viable AI apps and services. ✅ Computer vision ✅ Deep Learning ✅ Image Processing ✅ Machine Learning ✅ Generative AI ✅ Web Scraping ✅ Data Analysis 🚀 Whether you're launching a new computer vision or machine learning project or enhancing an established one, I aim to provide solutions that perfectly align with your objectives. As an experienced machine learning and computer vision engineer, I make the connection between high academic standards and real-world use. My M.Tech experience, which is based on modern artificial intelligence principles, enables me to transform both cutting-edge research and tried-and-true machine learning and deep learning models into solutions that are specifically suited for your challenges. Check out my offered services: 💢 Computer Vision: Deep Learning, Image Classification, Object Detection, Object Tracking, Pose Detection, Image Segmentation, and Image Processing Focusing on: ☑️ Live Object Detection with Webcam ☑️ Face and person re-identification (ReID) ☑️ Image Processing | Edge detection ☑️ Single/Multiple Object Tracking CCTV ☑️ Single/Multiple Image Classification ☑️ Pose Estimation | KeyPoint Detection ☑️ Action Recognition ☑️ Python GUI development ☑️ Image segmentation (semantic and instance) ☑️ OCR Image to Text 💢 Machine Learning: Data Analytics, Data Science, Predictive Analytics, Recommendation Systems, Anomaly Detection, Healthcare, Finance, Marketing, and Sales Focusing on: ☑️ Web Scraping ☑️ Data Analysis and Visualization ☑️ Feature engineering ☑️ Predictive Modeling ☑️ Recommendation Systems ☑️ Anomaly Detection ☑️ Time Series Forecasting ☑️ Model Deployment and Integration ☑️ Custom Machine Learning Solutions 💢 Notable Project Expertise Computer Vision, Image Classification, Object Detection, Image Segmentation, Pose Detecton, Medical Data Analysis, Face Detection, Video Analysis, Machine Learning and Other Deep Learning Projects ☑️ Object Detection using YOLO-NAS/ YOLOv8/ YOLOv7 ☑️ Object Detection and Tracking using YOLO-NAS / YOLOv8 / YOLOv7 and SORT/ DeepSORT / Bytetrack ☑️ Object Segmentation using YOLOv8 ☑️ Object Detection and Segmentation using YOLO-NAS and Segment Anything Model ☑️ YOLOv8 Segmentation with Multiple Object Tracking ☑️ Grocery Items Detection in a Retail Store with YOLO-NAS / YOLOv8 / YOLOv7 ☑️ Personal Protective Equipment Detection using YOLO-NAS / YOLOv8 / YOLOv7 ☑️ Vehicle Analytics with YOLO-NAS / YOLOv8 / YOLOv7 ☑️ African Wildlife Animals Detection and Tracking Using YOLO-NAS / YOLOv8 / YOLOv7 and Bytetrack/ DeepSORT/ SORT. ☑️ Personal-Protective-Equipment Detection Using Deep Learning and Python (YOLOv7) ☑️ Underwater (Fish and its types) Detection Using YOLO-NAS /YOLOv8 / YOLOv7 ☑️ AI Personal Trainer (Pushup, Biceps & Crunch Counting) Using MediaPipe. ☑️ Automatic Number Plate Recognition using YOLO-NAS/YOLOv8/YOLOv7 and Tesseract OCR / Paddle OCR / EasyOCR ( Images & Videos) ☑️ Fish Eye Camera People Detection and Object Tracking Using Machine Learning ☑️ Heatmap generation Using EigenCam and Object Detection (YOLOv8, YOLOv5) ☑️ Object Detection Using Transformers (RT-DETR) ☑️ Tennis Shots Identification and Counting using YOLOv7 Pose Estimation and LSTM Model ☑️ Vehicle Intensity Heatmaps using YOLO-NAS / YOLOv8 / YOLOv7 ☑️ Real Time Sign Language Detection with YOLO-NAS/ YOLOv8 / YOLOv7 and Webcam ☑️ Plastic Bottles Counting on Manufacturing Lines with YOLO-NAS / YOLOv8 / YOLOv7 and Object Tracking using SORT / DeepSORT / ByteTrack ☑️ People Face Detection, Segmentation, and Blurring Using YOLOv8 ☑️ Fruits Detection on Custom Dataset Using YOLO-NAS / YOLOv8 / YOLOv7 ☑️ Football Player Detection and Tracking Using YOLO-NAS / YOLOv8 and SORT / DeepSORT / ByteTrack ☑️ Multiple streams of object tracking and object detection. ☑️ Traffic Lights Detection and Color Recognition using YOLO-NAS / YOLOv8 ☑️ People Counter using YOLOv8 and Object Tracking using SORT/ DeepSORT/ Bytetrack ☑️ Potholes Detection using YOLOv8 (Images & Videos) ☑️ Potholes Detection and Segmentation using YOLOv8 (Images & Videos) ☑️ Fire and Smoke Detection using YOLO-NAS/ YOLOv8/ YOLOv7 ☑️ Waste Detection using YOLO-NAS / YOLOv8 / YOLOv7 ☑️ Waste Detection and Segmentation using YOLOv8 ☑️ Traffic Watch: Automated Vehicle Direction Detection and Counting using YOLOv8 ☑️ Face Detection, Gender Classification, Counting, Tracking, Alert and Analytics ☑️ Helmet Detection and Segmentation using YOLOv8 ☑️ Cracks Segmentation using YOLOv8. ☑️ Car Velocity Calculation + Vehicles Counting (Entering and Leaving) ☑️ Web App: Vehicles Counting using YOLO-NAS/YOLOv8/YOLOv7 and SORT/DeepSORT/Bytetrack Object Tracking ☑️ Streamlit App to Count the Vehicles Entering and Leaving 💬 Send me a message so we can discuss your projects! 😊 Looking forward to working with you!

I am a Software Developer with 2+ years of professional experience, in Machine learning based tasks. My overall experience is based on - ✅ Skillset : ✔Computer vision : RCNN, Faster R-CNN, Mask R-CNN, YOLO, OpenPose, MediaPipe, DeepSORT, Template Matching, DiffMOT. ✔ Natural Language Processing NLP) : Topic Modelling, Keyword Extraction, Knowledge Graphs, Named Entity Recognition, Sentiment Analysis, Text Summarization. ✔ Generative AI : Fine tune Gemma-2 model, Fine tune Llama-3 model, Tracking Training with Perplexity, Retrieval Augmented Generation (RAG), RAG-with-SubQuestionQuery, RAGwith Multi-Query, RAG-Decomposition, RAG-withHyDE-Query-Transformation, RagFusion, RAG withStep-Back, RAG-with-Cohere-and-ReRank. ✔ Have experienced on publishing two Research paper in the field of machine learning.