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Hello! I’m Shahariar, a Machine Learning Engineer and AI Specialist passionate about developing intelligent systems that bridge the gap between artificial intelligence and real-world applications. With 4+ years of experience in computer vision, deep learning, and AI-powered automation, I have worked on a wide range of projects, from real-time object detection and facial recognition to image segmentation and AI-driven mobile applications. My expertise includes designing and training custom AI models, optimizing deep learning frameworks for efficiency, and deploying machine learning solutions on cloud and edge devices. I have successfully built OCR-based AI systems, 3D facial landmark detection models, and autonomous object detection applications for industries like security, agriculture, and augmented reality. I stay ahead of the curve by working with cutting-edge technologies like YOLO, TensorFlow Lite, MediaPipe, and OpenCV, ensuring high-performance AI solutions that meet industry standards. Beyond AI, I am also a 360° & 180° VR Video Editor, blending creativity with technology to create immersive storytelling experiences. From virtual real estate tours to interactive tourism content, I help businesses enhance audience engagement through dynamic VR media. My mission is simple: deliver high-performance AI solutions and immersive media that drive success. Whether you're looking to build an AI-powered application, annotate complex datasets, or create an engaging VR experience, I am here to bring your vision to life. 🔹 Machine Learning & AI Services ✔ Computer Vision & Object Detection – Bounding boxes, polygons, segmentation, keypoints ✔ Deep Learning & Neural Networks – TensorFlow, PyTorch, OpenCV, YOLO ✔ AI-Powered Mobile Applications – Model deployment on Android & iOS (TFLite, CoreML) ✔ OCR & Text Recognition – Number plate recognition, document text extraction (EasyOCR, Tesseract) ✔ 3D Facial Landmark Detection – AI-driven face mapping for AR, beauty filters, and security ✔ Custom AI Model Training – Data preprocessing, model optimization, fine-tuning, deployment ✔ NLP & Text Analysis – Named Entity Recognition (NER), sentiment analysis, AI chatbots ✔ Image Annotation & Data Labeling – Bounding boxes, polygons, segmentation, classification 📌 Notable AI/ML Projects 1️⃣ AI-Based Number Plate Recognition System ✅ Built a real-time OCR system for detecting vehicle number plates using EasyOCR & OpenCV. ✅ Optimized for automated traffic monitoring, law enforcement & toll collection. 2️⃣ Real-Time Facial Landmark Detection (3D Face Mapping) ✅ Integrated Google MediaPipe Face Mesh to track 468 3D facial landmarks. ✅ Used for AR applications, facial recognition & AI-powered beauty filters. 3️⃣ YOLO Object Detection for Smart Agriculture ✅ Developed an AI-powered mobile app to classify good, damaged & fallen apples in real-time. ✅ Utilized YOLO & TensorFlow Lite to help farmers monitor apple quality & predict yield. 🎥 360° & 180° VR Video Editing Services ✔ Virtual Reality Storytelling – Creating engaging 360° & 180° experiences ✔ Real Estate Virtual Tours – High-quality VR walkthroughs for property marketing ✔ Travel & Tourism VR Content – Immersive 360° videos for hotels, cities & attractions ✔ VR for Gaming & Entertainment – Enhanced interactive storytelling & immersive media ✔ Corporate & Product Presentations – Engaging VR-based promotional videos 🛠 Editing Tools: Adobe Premiere Pro, After Effects, Mistika VR, DaVinci Resolve, Insta360 Studio 🛠 Tools & Technologies 🔹 Machine Learning & Deep Learning: TensorFlow, PyTorch, Scikit-learn 🔹 Computer Vision: OpenCV, YOLO, MediaPipe, EasyOCR 🔹 NLP & Text Processing: spaCy, NLTK, Hugging Face Transformers 🔹 Image Annotation Tools: CVAT, LabelMe, LabelBox, Supervisely 🔹 VR & 360° Video Editing: Adobe Premiere Pro, Mistika VR, DaVinci Resolve 🔹 Cloud & Deployment: AWS, Google Cloud, Flask, FastAPI ✨ Why Work with Me? ✔ End-to-End AI Development – From data processing to model deployment ✔ High-Performance AI Models – Optimized for mobile, cloud, and edge computing ✔ Expert in AI-Powered Media – Blending AI with immersive storytelling ✔ Client Satisfaction Guaranteed – Fast delivery, clear communication & unlimited revisions 📩 Let’s Build Something Great! If you need a Machine Learning Engineer who can develop AI-powered applications, or a VR Video Editor who can bring immersive experiences to life, let's connect! 💬 Send me a message today to discuss your project! 🚀

Give me something to build and I will build it for u in a way that it has no bug. If you shoot me a message or invitation I can send you a personalized loom video. Hey, I am Abid, a recent Computer Science graduate with hands-on experience in Artificial Intelligence, Machine Learning, Large Language Models (LLMs), and website development. My expertise includes: ✔ Large Language Models (LLMs) and Generative AI ✔ Prompt Engineering and AI Evaluation ✔ Hugging Face ✔ Machine Learning & Deep Learning ✔ Computer Vision and Convolutional Neural Networks (CNNs) ✔ Knowledge Distillation and Model Optimization ✔ Hugging Face ✔ Python, NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow ✔ Data Analysis and Data Preprocessing ✔ Website and project Development ✔ Teaching I have been involved in projects related to training models, evaluation, data labeling, AI testing, and machine learning research. I am enthusiastic about developing robust, efficient, and scalable AI solutions. If you need any help with the development of AI models, applications of computer vision, data analysis, or machine learning research, I am available for your support.

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'm a Machine Learning Engineer with a 2.5+ years of experience. I have passion for data cleaning and creating models that make a difference in people's lives. I specialize in OCR systems, Medical Image Analysis, Raw Data Analysis, Data Cleaning, Generative AI and any Computer Vision related works. I have extensive experience with Python, Flask, Pandas, Scikit-Learn, PyTorch. I have expertise in ComfyUI, Custom Node Management, LoRa fine tuning of Stable Diffusion and Flux, Consistent character generation, Text2Img, Img2Img, Text2Video, Img2Video, Image Enhancing, Image Upscaling and many more. I also have skills on CrewAI for AI Agent development.

Hello there!👋 I'm Sajal Das, a Machine Learning Engineer with 2+ years of expertise in ML research, Natural Language Processing, Image Processing and Deep Learning and Web Automation & Scraping. I am ML Engineer with: 🥇 2 years of experience. 🥇 20+ completed projects My services are: 👨🏻‍💻 Data Handling and Data Visualization 👨🏻‍💻 Machine Learning Model build 👨🏻‍💻 Deep Learning Model build 👨🏻‍💻 Interactive Python script 👨🏻‍💻 Model Integration with APIs (Python) 👨🏻‍💻 Browser Automation 👨🏻‍💻 Web Scraping and Data Extraction I work with the following libraries: 🎯 Data Handling: Pandas, Numpy, Scrapy, BeautifulSoup, Selenium 🎯 Visualization tools: Plotly, Seaborn and Matplotlib 🎯 Frameworks: TensorFlow, Keras, PyTorch, NLTk 🎯 Databases and Big Data: SQLite, PostgreSQL, Hadoop, Spark I put a lot of effort into: ✔ Effectively understanding the demands of your project ✔️ Regularly interact during the entire project ✔️ On-time delivery of quality Send me a message; I'm available and prepared to work right away.

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.