Hire the Best NVIDIA Mental Ray Specialists
in Bangladesh

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.

Hi, I’m Mohini Sultana. I’m skilled Data Annotator with 5+ years of experience in image/video labeling, AI data processing, Satellite Image Annotator, virtual assistance, and data entry. I’m reliable, deadline-driven, and ready to work across any time zone. I specialize in prioritizing tasks and solving problems efficiently to ensure high-quality results. Common Types of Annotations: * Bounding Boxes – Draw rectangles around objects. * Polygon Annotation – Outline the exact shape of objects. * Semantic Segmentation – Label every pixel in an image with a class (e.g., road, car, person). * Instance Segmentation – Like semantic, but distinguishes between object instances * Keypoint Annotation – Mark specific points (e.g., facial landmarks, human joints). * Classification – Assign a label to the whole image (e.g., “cat” or “dog”). * Object Counting – Count how many of a specific object are present. * Lane or Line Annotation – For road detection, lane marking. List of the tools that I learned and used: ✔️ CVAT ✔️ Roboflow ✔️ SuperAnnotate ✔️ Darwin V7 ✔️ Supervisely ✔️ Labelbox ✔️ Labelimg ✔️ Labelme ✔️Scale Pro ✔️Dataloop Ai ✔️QGIS ✔️ Canva ✔️ Filmora ✔️Photoshop ✔️Canva Skills: - Prepares a dataset for computer vision or machine learning and models. - AI Image annotation/Video Annotation - AI Data Processing - AI Annotation format converting - Image Masking - Image Segmentation - Categorization - Fact-Checking Annotation - Data entry - Virtual assistant - Data scraping While I am grateful for the opportunities afforded to me in my prior position, I am ready to embark on a new career path that offers new challenges for me to put my skills to use. #Data annotation #image Annotation #Data Labeling #Data Annotation Specialists #Annotation Specialists #Cvat #Superannotate #Image Segmentation #Video Annotation #Roboflow #Data Annotator I'm a very competitive person and a fast learner. Rest assured, I provide high-quality output, and also I can do tasks as fast and accurately as possible. Your project's success starts with the right data annotation, and I am here to provide just that.

AI Data Annotation Specialist with experience delivering high-quality training data for Computer Vision, NLP, and LLM projects. I have successfully completed 30+ annotation projects across autonomous vehicles, warehouse safety, medical imaging, sports analytics, surgical tools, and object detection. My expertise includes: • Bounding Box, Polygon, Semantic & Instance Segmentation • Image & Video Annotation, Object Tracking, Keypoint Labeling • OCR & Text Annotation • NLP Data Annotation & Named Entity Recognition (NER) • LLM Training Data Preparation & Prompt/Response Evaluation • Sentiment Analysis & Text Classification • Quality Assurance & Accuracy Review Proficient in CVAT, Encord, Supervisely, SuperbAI, and other leading annotation platforms. I focus on accuracy, consistency, and scalable project delivery while maintaining clear communication and meeting deadlines. Whether you need a dedicated annotator or support for large-scale AI training datasets, I can help deliver reliable, high-quality results.

With extensive experience in image and data annotation, I have successfully completed over 130+ outsourced projects in remote job at Q-AI. My expertise includes: Image Annotation 🖼️: Utilizing bounding boxes, polygons, and semantic annotations to precisely label images as per client requirements. Video Annotation 🎥: Detailed frame-by-frame annotation for accurate object tracking and analysis. Annotation Types ✏️: Proficient in creating annotations using polygons, bounding boxes, dots, lines, and circles. Export Formats 📂: Capable of exporting results in various formats, including JSON, YOLO/Text, Pascal/VOC/XML, and COCO. Professional Annotation Tools 🛠️: LabelMe ImageBox QuPath Hasty.ai CVAT VoTT Customized Annotation Tools Data Types 🗂️: Images 📸 Videos 📹 With a commitment to accuracy and efficiency, I ensure that all annotated data meets the highest standards, facilitating better training for machine learning models. Whether you need detailed polygon annotations or precise bounding boxes, I am equipped to deliver top-notch results tailored to your specifications. Feel free to adjust any part of this summary to better fit your personal style or specific experiences!

Need annotation done right the first time with zero quality issues? That's exactly what I deliver. I'm a Data Annotation Specialist with hands-on experience in audio/speech, image/video, and sentiment & intent labeling plus a Quality Assurance background that means I don't just label data, I verify it meets production-ready standards. I hold a C2 (Proficient) level in English, making me especially effective on English-language NLP, transcription, and sentiment projects where linguistic nuance directly impacts model accuracy. My 100% Job Success Score on Upwork reflects the consistency and reliability clients come back for. What sets me apart from other annotators: ✅ QA-Trained Eye — I've worked as a Quality Analyst on annotation projects, reviewing labeled datasets for consistency, guideline compliance, and edge-case errors before they reach your model ✅ Project Handling Experience — I've managed annotation workflows end-to-end, not just labeled tasks — so I understand timelines, batching, and delivery pipelines from a lead perspective ✅ C2 English Proficiency — Ideal for transcription, audio labeling, sentiment tagging, intent classification, and any task where English accuracy is non-negotiable ✅ 100% Job Success Score — Every client. Every project. Delivered right. ✅ Platform Fluency — Labelbox, Scale AI / Appen, Excel & Google Sheets Annotation types I cover: Audio & Speech — Transcription, speaker diarization, noise/silence tagging, ASR dataset labeling Image & Video — Bounding boxes, polygon segmentation, object classification, frame labeling Sentiment & Intent — Emotion tagging, intent classification, NLP dataset preparation, chatbot training data My promise to you: Clean data. Met deadlines. Clear communication. And a QA mindset that catches problems before they cost you time and money. 📩 Tell me about your dataset — I'll tell you exactly how I can help.

Hello there, I am Shahriar Refath, a distinguished Image and Data Annotator with a robust portfolio spanning over three years of professional experience. Known for meticulous approach and expertise in high-precision annotation techniques, I can bring unparalleled value to AI and machine learning projects. Expertise Highlights: 1. High-Precision Annotation Techniques: I am specialized in high-precision annotation techniques, including bounding boxes, polygons, and semantic segmentation. His keen attention to detail ensures accuracy crucial for AI and machine learning models. 2. Diverse Annotation Tool Proficiency: With proficiency in various annotation tools such as CVAT, Roboflow, LabelImg, and more, I can leverage the best tools to deliver exceptional results tailored to project needs. 3. Comprehensive Annotation Solutions: My expertise covers a wide range of annotation solutions, from bounding boxes and object labeling to sentiment analysis and image classification, ensuring comprehensive coverage for diverse project requirements. 4. Versatile Data Output Formats: I can deliver annotated data in various formats, including Pascal VOC, YOLO, JSON, COCO Format, CSV File, and segmentation masks, ensuring compatibility with different AI frameworks. My proficiency in image and data annotation, coupled with commitment to precision and versatility, makes me a valuable asset for any AI and machine learning project. With my expertise, projects are empowered to achieve optimal performance and results.