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Expert-Vetted Top 1% on Upwork | Top 10 Machine Learning Agency on Upwork | 7+ Years in Production AI | Sports, Industrial, Satellite, Healthcare I'm a Computer Vision Engineer and Machine Learning Engineer with 7+ years delivering production-grade AI systems. Upwork has Expert-Vetted me as a Top 1% specialist in this niche, and our team is ranked among the Top 10 Machine Learning agencies on Upwork. I work with product teams and startups across sports analytics, industrial inspection, satellite and aerial imagery, access control, healthcare, and generative AI — any domain where visual data needs to become reliable, actionable output running in production. As a Computer Vision Engineer, my core work covers object detection, multi-object tracking, pose estimation, image segmentation, image processing, and real-time video analysis. I build end-to-end pipelines in Python using OpenCV, PyTorch, TensorFlow, and Keras, from dataset preparation and model training through TensorRT optimization and Docker deployment on cloud or NVIDIA Jetson edge hardware. I use C++ for performance-critical components where Python latency is a bottleneck. The domains where computer vision engineer experience creates the most value: sports analytics (player tracking, performance metrics, automated statistics from broadcast video), industrial inspection (defect detection and quality control on production lines), satellite and aerial imagery (object detection and segmentation for infrastructure analysis), access control and security (vehicle identification, multi-camera real-time monitoring), and healthcare and biomechanics (pose analysis, body measurement, and biomedical signal processing connected to AI coaching backends). As a Machine Learning Engineer and Data Scientist, I also build systems for structured and time-series data: demand forecasting, anomaly detection, biomedical signal analysis, and structural health monitoring. My data scientist workflow covers scikit-learn, pandas, NumPy, and SciPy alongside deep learning frameworks, with experiment tracking and evaluation metrics to ensure models perform consistently in production. When projects require generative AI or LLM components, I deliver RAG pipelines with LangChain and vector databases, synthetic dataset generation tools, and document processing systems using the Gemini API. Regardless of domain, the computer vision engineer approach stays the same: combine OpenCV-based preprocessing with deep learning inference into a scalable, testable pipeline that holds up under real-world conditions — variable lighting, occlusion, low resolution, multi-camera setups, and edge hardware constraints. I work with YOLO-family models, ByteTrack and DeepSORT for tracking, MediaPipe and MMPose for pose estimation, TensorRT and ONNX for inference optimization, and FastAPI with Docker for production deployment. I work with a specialized team that includes a computer vision PhD, deep learning researchers, and mathematical optimization specialists. This lets me scope complex systems, split parallel workstreams, and deliver a full Computer Vision Engineer engagement faster than a solo contributor could. Clients typically work with me when they need: - a Computer Vision Engineer to build detection, tracking, or segmentation systems from scratch - a Machine Learning Engineer to productionize a research model and meet latency requirements - a Data Scientist who can go from raw data to a deployable model end-to-end - an AI engineer to integrate LLM or generative components into an existing backend - real-time or edge inference optimized for NVIDIA Jetson or mobile deployment - a Python developer who understands both the AI pipeline and the surrounding system architecture If you need a Computer Vision Engineer with the full stack from dataset to deployed API, let's talk. Main stack: Python, OpenCV, PyTorch, TensorFlow, Keras, YOLO, ByteTrack, MediaPipe, MMPose, CoreML, TFLite, TensorRT, ONNX, scikit-learn, FastAPI, Docker, PostgreSQL, C++, NumPy, pandas, SciPy.

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.

🔹 Top Rated Machine Learning Engineer | Expert in Detection, Tracking, Classification & OCR I specialize in building high-accuracy computer vision models — from object detection and classification to keypoint detection and OCR. With deep experience in YOLO (v8–v11), TensorFlow, and PyTorch, I’ve delivered results across industries including healthcare, logistics, and agriculture. 🚀 Highlighted Projects: 🔍 License Plate Recognition & Number Swapping — for Korean and Kazakh vehicles 🏥 COVID-19 & Viral Pneumonia Detection — 95%+ accuracy using X-ray images 🍎 Fruit Detection (Apple, Peach, Potato) — precision object detection with YOLO 📄 OCR & Keypoint Detection — paper/card ID localization and tracking 🏎️ Speed Estimation & Vehicle Tracking — model fusion using YOLO + Deep SORT ⚙️ Core Skills & Tools: YOLOv5/v8 | TensorFlow | PyTorch | OpenCV | ONNX Object Detection, Classification, OCR, Keypoint Detection High-speed model training on RTX 4080 Super As a Top Rated freelancer, I deliver clean, efficient, and production-ready models on time and with clear communication. Let’s bring your vision to life. 📩 Message me — I respond quickly and build fast.

✅ EXPERT-VETTED 🎖 TOP RATED PLUS (Top 1% 🥇 of GLOBAL talent on Upwork). I am a Data Scientist, AI, Machine Learning, and Software Engineer with over 9 years of professional experience building software at any scale. I used to work at large companies such as Yandex as well as in many successful startups. Now I am running Shapeion Technologies, providing software and ML consulting for companies around the globe, to accomplish their business goals. I have been managing startup projects with client teams and have helped draft product roadmaps, planning, and talent acquisition. I have a good network of engineer talent who can help build product MVPs effectively. Products made by me helped startups to raise millions of USD. I have a team of 15 professional Data Annotation specialists who have completed many projects in the Natural Language Processing and Computer Vision fields. They are fluent in the following data annotation platforms: CVAT, Doccano, and ProdiGy. Here is a brief summary of my expertise: ● 2000+ worked hours and 30+ projects completed PERSONALLY only at Upwork ● Created MVPs for various startups and companies that raised millions of USD ● Helped 30+ businesses to reach their goals in time I am proficient in: ✅ Data Science and Machine Learning: ● classification and regression tasks ● time-series prediction (ARIMA, ...) ✅ Natural Language Processing (NLP): ● Text classification (including Sentiment Analysis, Aspect-Based Sentiment Analysis) ● Named Entity Recognition (NER, automatic extraction of relevant entities, like names, locations, dates, etc) ● Chat-Bots (Deep Learning or Rule-Based chat-bots for various domains) Frameworks: SpaCy, HuggingFace, Transformers, NLTK, Langchain, Pinecone Annotation platforms: Doccano, ProdiGy, Clarifai Architectures: BERT, LongFormer, GPT-3, GPT-4 Domains: MedTech, E-Commerce, HRM, AD-tech ✅ Computer Vision (CV): ● Image Classification ● Object Detection (YoLo) ● Instance segmentation / Semantic Segmentation (Mask R-CNN) ● Optical Character Recognition (OCR) Frameworks: PyTorch, TensorFlow, Keras, DarkNet, Detectron, Detectron2, Fast.ai Annotation platforms: CVAT, Clarifai Domains: Medical Imaging, CCTV (surveillance), NFT, LegalTech (document analysis) ✅ Full-Stack Development: ● Backend (FastAPI, Flask, Django, django-rest-framework, Node.js, JavaScript, PHP, Yii Framework) ● Frontend (Vue.js, React.js) ● WordPress ● CI/CD (Docker, docker-compose, Kubernetes, k8s, Ansible, Terraform) ● Databases (PostgreSQL, MySql, MongoDB, OracleDB, ArangoDB, ClickHouse, GraphDB) ● AMQ (Celery, RabbitMQ) Technical skills: Python, Javascript, Go, Java, Spring Boot, Gradle, Node.js, Angular, Tensorflow, Keras, NumPy, Serverless Frameworks, Docker, Terraform, Ansible, Kubernetes, Jenkins, Spinnaker, GitOps, DataOps, AWS. GCP, Azure, JAMstack, GraphQL, MEAN Stack, React, Redux, ES6, Express, MongoDB, Redis, Linux, Etcd, Consul, Kinesis, Redshift, DynamoDB, Spark, Hadoop, Kafka, PostgreSQL, MySQL, ELK, Git, Flask, and Django. Thanks for your consideration! "Alexander was excellent. He managed expectations well and overperformed on the work. Communications were excellent. He saved us 100s of hours of work by automating our processes. He's more expensive than many people on Upwork, but appears to be exceptionally good an reliable." - Tom Mather

AI helps businesses move faster, automate operations, improve customer experience, and create new revenue opportunities. As an AI Developer, AI Consultant, and founder of an AI SAAS development company, I help startups and established businesses turn ideas into production-ready AI products For 10+ years, our team has been building software products, automation platforms, and intelligent systems. We combine the expertise of AI Developer teams, AI Consultant services, AI SAAS architects, AI Chatbot engineers, machine learning specialists, data scientist professionals, web developer teams, python developer experts, and web application developer resources to deliver scalable business solutions Key company metrics: • 10+ years in software development • 200+ successfully delivered projects • 20,000+ tracked hours on Upwork • 40+ software professionals • $1M+ earned on the platform • 5+ years average developer experience Today our team includes AI Developer specialists, machine learning engineers, data scientist professionals, python developer experts, web developer teams, and web application developer specialists. The average engineer brings 5+ years of commercial experience building AI SAAS platforms, AI Chatbot products, and business automation systems Our work focuses on helping companies implement AI in practical ways. We build AI SAAS products, AI Chatbot solutions, machine learning systems, natural language processing applications, small business automation platforms, AI API integrations, GPT-3 assistants, and intelligent workflow automation tools Many companies search for an AI Developer when what they really need is an AI Consultant who can connect technology to business outcomes. Successful AI implementation requires more than a model. It requires machine learning, natural language processing, data scientist expertise, AI API architecture, product strategy, and scalable software development. That is the approach we bring to every engagement Our AI Developer teams have delivered AI SAAS platforms used for customer support, internal operations, sales enablement, knowledge management, analytics, and workflow automation. We regularly build AI Chatbot systems using GPT-3, Botpress, AI API integrations, machine learning models, and natural language processing pipelines that improve customer engagement and reduce manual work As an AI Consultant, I help companies identify where AI creates measurable value. In many cases this includes small business automation, AI Chatbot implementation, AI API integration, GPT-3 assistants, machine learning models, and intelligent process automation. These solutions help teams save time, improve accuracy, and scale operations without increasing headcount Several projects have involved advanced machine learning, natural language processing, deep voice technologies, deep voice over solutions, conversational AI, and intelligent assistants designed to act as a virtual deal closer for sales teams. Our AI Developer teams work closely with founders and stakeholders to ensure every solution delivers business results rather than technology for its own sake Over the years, our AI Developer teams have worked with startups, SaaS companies, agencies, and enterprise organizations seeking practical AI adoption. As an AI Consultant, I regularly help businesses identify machine learning opportunities, evaluate AI API integrations, and design AI SAAS products that generate measurable ROI. Our AI Developer specialists collaborate with data scientist teams, python developer experts, web developer professionals, and web application developer engineers to transform ideas into scalable products Our experience includes AI Chatbot development for customer support, sales enablement, lead qualification, internal knowledge management, and small business automation. We have implemented GPT-3 solutions, Botpress platforms, natural language processing workflows, deep voice integrations, and deep voice over technologies for organizations looking to improve customer communication and operational efficiency Whether you need an AI Developer to build a new product, an AI Consultant to validate an idea, an AI SAAS partner to scale an existing platform, or an AI Chatbot team to improve customer interactions, we can help. Our expertise combines machine learning, data scientist capabilities, python developer experience, web developer execution, web application developer expertise, AI API integrations, GPT-3 technologies, Botpress solutions, natural language processing, small business automation, deep voice systems, and enterprise-grade software engineering We believe the best AI products are built around measurable outcomes. That is why every AI Developer, AI Consultant, AI SAAS architect, and AI Chatbot engineer on our team focuses on creating solutions that improve efficiency, automate repetitive work, and generate long-term value for our clients

AI / ML / Agentic AI LLM / Langchain / RAG ChatBots AI / Machine Learning & Computer Vision specialist with hands-on experience building, training, and deploying deep learning models for real-world applications. I specialize in YOLO-based object detection/segmentation and image processing, with a strong background in PyTorch, TensorFlow, and OpenCV, Plus modern RAG and agentic AI LLM systems using LangChain and LangGraph. Key Expertise YOLO Object Detection & Segmentation: Custom training/tuning (YOLO v8–v12), dataset prep, evaluation, and edge AI deployment on Jetson Nano. RAG & Agentic AI: Retrieval-augmented generation with LangChain/LangGraph, vector stores (Pinecone, ChromaDB), tool use, memory, and guardrails. Recommendation AI Engine Systems. Model Deployment & Optimization: Packaging for production (REST), quantization/acceleration where applicable; VM-based deployments. Full Pipeline Development: Data collection → labeling → training → testing → Flask/FastAPI REST APIs → monitoring. MLOps & CI/CD: Experiment tracking, versioning, reproducible workflows, and automated delivery with GitHub/GitLab. API Integration & Apps: Secure API integration with third-party services; Flutter front-ends that consume ML APIs. Cloud & Storage: AWS S3 for datasets/artifacts; deployable on cloud VMs or on-prem. Frameworks & Tools PyTorch, TensorFlow/TFLite, OpenCV, scikit-learn, Pandas, NumPy LangChain, LangGraph, RAG, LLM, Pinecone, ChromaDB Flask, FastAPI (REST API deployment) Databases SQL, MySQL, PostgreSQL, Supabase GitHub, GitLab (MLOps/CI-CD) Google Colab / Jupyter Notebook AWS S3, VM-based deployments React & Flutter (client apps & dashboards)