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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.

"𝐑𝐨𝐜𝐤 𝐬𝐭𝐚𝐫 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫, 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐞𝐭𝐬 𝐭𝐨 𝐤𝐧𝐨𝐰 𝐭𝐡𝐞 𝐦𝐢𝐬𝐬𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐞𝐱𝐞𝐜𝐮𝐭𝐞𝐬 𝐰𝐢𝐭𝐡 𝐯𝐞𝐫𝐚𝐜𝐢𝐭𝐲!" ⭐⭐ ⭐ ⭐ ⭐ 𝟓 — 𝐂𝐥𝐢𝐞𝐧𝐭 𝐑𝐞𝐯𝐢𝐞𝐰, 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 (𝐌𝐚𝐫–𝐀𝐩𝐫 𝟐𝟎𝟐𝟓) Hi, I’m an AI Agent Developer with hands-on technical expertise and an MBA-trained strategic mindset. I build multi-agent LLM systems, RAG-powered assistants, and end-to-end automation pipelines that don't just function — they scale. I don’t just build AI systems — I align them with your growth goals, user experience, and ROI. Whether it’s an LLM-based assistant, a voice agent that drives revenue, or a full-stack multi-agent workflow — I deliver end-to-end ownership from design to deployment. 💼 𝐖𝐡𝐚𝐭 𝐈 𝐂𝐚𝐧 𝐁𝐮𝐢𝐥𝐝 𝐅𝐨𝐫 𝐘𝐨𝐮 🤖 𝐋𝐋𝐌-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐂𝐫𝐞𝐰𝐀𝐈, 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡, 𝐀𝐮𝐭𝐨𝐠𝐞𝐧, 𝐎𝐩𝐞𝐧𝐀𝐈 𝐒𝐃𝐊) 🗣️ 𝐕𝐨𝐢𝐜𝐞 + 𝐀𝐜𝐭𝐢𝐨𝐧 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐕𝐚𝐩𝐢.𝐚𝐢, 𝐄𝐥𝐞𝐯𝐞𝐧𝐋𝐚𝐛𝐬, 𝐃𝐞𝐞𝐩𝐠𝐫𝐚𝐦, 𝐏𝐥𝐚𝐲𝐇𝐓) 📚 𝐑𝐀𝐆 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐁𝐌𝟐𝟓 + 𝐃𝐞𝐧𝐬𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥, 𝐂𝐡𝐫𝐨𝐦𝐚, 𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞, 𝐖𝐞𝐚𝐯𝐢𝐚𝐭𝐞, 𝐐𝐝𝐫𝐚𝐧𝐭) 🧩 𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 𝐀𝐠𝐞𝐧𝐭 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 (𝐅𝐚𝐬𝐭𝐀𝐏𝐈 + 𝐑𝐞𝐚𝐜𝐭.𝐣𝐬 + 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐭) 🔗 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 (𝐧𝟖𝐧, 𝐙𝐚𝐩𝐢𝐞𝐫, 𝐌𝐚𝐤𝐞.𝐜𝐨𝐦, 𝐀𝐢𝐫𝐭𝐚𝐛𝐥𝐞, 𝐒𝐮𝐩𝐚𝐛𝐚𝐬𝐞, 𝐏𝐢𝐩𝐞𝐝𝐫𝐞𝐚𝐦) 🧠 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐞𝐚𝐫𝐜𝐡 𝐀𝐩𝐩𝐬 (𝐜𝐮𝐬𝐭𝐨𝐦 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬, 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐐&𝐀, 𝐦𝐞𝐦𝐨𝐫𝐲 𝐚𝐠𝐞𝐧𝐭𝐬) 📊 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 & 𝐀𝐠𝐞𝐧𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐬 🧰 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: 𝐂𝐫𝐞𝐰𝐀𝐈, 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧, 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡, 𝐀𝐮𝐭𝐨𝐠𝐞𝐧, 𝐀𝐠𝐞𝐧𝐭𝐐𝐋, 𝐎𝐩𝐞𝐧𝐀𝐈 𝐒𝐃𝐊, 𝐕𝐚𝐩𝐢.𝐚𝐢, 𝐌𝐚𝐤𝐞.𝐜𝐨𝐦, 𝐍𝟖𝐍 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐋𝐋𝐌 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 𝐆𝐏𝐓-𝟒, 𝐂𝐥𝐚𝐮𝐝𝐞, 𝐆𝐞𝐦𝐢𝐧𝐢, 𝐌𝐢𝐬𝐭𝐫𝐚𝐥, 𝐎𝐩𝐞𝐧𝐑𝐨𝐮𝐭𝐞𝐫, 𝐎𝐥𝐥𝐚𝐦𝐚 𝐕𝐞𝐜𝐭𝐨𝐫 & 𝐑𝐀𝐆 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬: 𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞, 𝐐𝐝𝐫𝐚𝐧𝐭, 𝐖𝐞𝐚𝐯𝐢𝐚𝐭𝐞, 𝐂𝐡𝐫𝐨𝐦𝐚, 𝐌𝐢𝐥𝐯𝐮𝐬, 𝐇𝐲𝐛𝐫𝐢𝐝 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 (𝐁𝐌𝟐𝟓 + 𝐃𝐞𝐧𝐬𝐞) 𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤 𝐃𝐞𝐯: 𝐏𝐲𝐭𝐡𝐨𝐧, 𝐅𝐚𝐬𝐭𝐀𝐏𝐈, 𝐅𝐥𝐚𝐬𝐤, 𝐍𝐨𝐝𝐞.𝐣𝐬, 𝐑𝐞𝐚𝐜𝐭.𝐣𝐬, 𝐍𝐞𝐱𝐭.𝐣𝐬, 𝐓𝐚𝐢𝐥𝐰𝐢𝐧𝐝 𝐂𝐒𝐒, 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐭, 𝐑𝐚𝐢𝐥𝐰𝐚𝐲, 𝐕𝐞𝐫𝐜𝐞𝐥 𝐃𝐞𝐯𝐎𝐩𝐬 & 𝐈𝐧𝐟𝐫𝐚: 𝐃𝐨𝐜𝐤𝐞𝐫, 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬, 𝐂𝐈/𝐂𝐃 (𝐆𝐢𝐭𝐇𝐮𝐛 𝐀𝐜𝐭𝐢𝐨𝐧𝐬, 𝐆𝐢𝐭𝐋𝐚𝐛), 𝐀𝐖𝐒, 𝐆𝐂𝐏, 𝐀𝐳𝐮𝐫𝐞 🎓 Business & Strategy ✅ MBA + Engineering Mindset: Deep understanding of market fit, cost-efficiency, and product-market alignment 📈 Results-Driven Development: 200%+ ROI, 40% reduction in operational costs, 6x content productivity 🎯 Roadmap-First Approach: I help startups and founders scope and sequence AI features with clear KPIs 🧩 Product Strategy Support: Go beyond coding — get business insights, market positioning, and UX thinking baked into every build ✅ 𝐖𝐡𝐲 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 𝐂𝐡𝐨𝐨𝐬𝐞 𝐌𝐞 💼 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 + 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 🧩 𝐌𝐨𝐝𝐮𝐥𝐚𝐫 & 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 💬 𝐅𝐚𝐬𝐭 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧, 𝐂𝐥𝐞𝐚𝐫 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 🔒 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐢𝐭𝐲 𝐆𝐮𝐚𝐫𝐚𝐧𝐭𝐞𝐞𝐝, 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞-𝐑𝐞𝐚𝐝𝐲 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 Let’s build your agent the right way — from prototype to production.

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! 🚀

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 Senior Computer Vision Engineer with 7+ years of professional experience delivering enterprise-grade Computer Vision, Edge AI, Deep Learning & Machine Learning Solutions. I have a solid foundation in state-of-the-art deep learning models and machine learning algorithms, applying them to build, deploy, and optimize production-ready systems across edge devices, cloud platforms, and agentic AI pipelines. ✅ What I Build ▸ Computer Vision: object detection, tracking, segmentation, pose estimation, image classification, counting, OCR, anomaly detection ▸ Edge AI: NVIDIA Jetson (Nano, Orin, Xavier), Raspberry Pi, model optimization with TensorRT, ONNX, and TFLite for real-time inference ▸ Image Generation: Stable Diffusion (SDXL, SD 3.5), Flux, DALL·E 3, ControlNet, LoRA fine-tuning, ComfyUI workflows ▸ Video Generation: Kling, Runway Gen-3, Minimax Hailuo, Veo, Pika, automated cinematic and product video pipelines ▸ AI Agents: autonomous multi-step agents using LangChain, LangGraph, CrewAI, and AutoGen with RAG, memory, and tool use ▸ Voice AI: conversational voice agents using Vapi, Retell AI, ElevenLabs, Bland AI, and Deepgram for inbound/outbound calling and automation ▸ MCP Servers: custom Model Context Protocol servers connecting Claude and other agents to your APIs, databases, and internal tools ▸ Claude Code: agentic software engineering with subagent architectures, skill-based pipelines, and autonomous multi-step coding workflows ✅ Tech Stack ▪ Computer Vision: OpenCV, YOLO, MediaPipe, Detectron2, SAM, Vision Transformers ▪ Deep Learning: PyTorch, TensorFlow, Keras, ONNX ▪ Tracking & Optimization: DeepSORT, ByteTrack, TensorRT, OpenVINO ▪ Deployment: DeepStream, Triton Inference Server, TFLite, FastAPI, Flask, Docker ▪ Generative AI: Stable Diffusion, Flux, ComfyUI, Replicate, Fal.ai ▪ Agents & Voice: LangChain, LangGraph, CrewAI, Vapi, Retell AI, ElevenLabs, n8n ▪ MCP & Agentic: Claude Code, MCP SDK, custom tool servers ▪ Cloud: AWS, GCP, Azure ▪ Languages: Python, C++, CUDA 🚀 Send me a message with what you are trying to build, and let's discuss.

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.