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I help researchers, startups, and businesses turn AI ideas into working MVPs and scalable products through machine learning development, AI model integration, AI/Backend Engineer, Workflow Automations and research-backed implementation & writing support. 🏆 Top Rated | 100% Job Success | about $10K+ Earned | 40+ Completed Upwork Jobs Whether you need to validate an AI idea, integrate an existing model, reproduce research code, or improve an ML pipeline, I can help move your project from concept to a reliable implementation. 🏆 MVPs to Scalable Products | AI Research & Development | Research Support-Asistance 🧠 Deep Learning | Machine Learning | ML Model Training & Fine-Tuning | APIs | LLMs 🌟 2D/3D Vision | Sensors and Medical Data | GitHub & Hugging Face Code Reproduction 🧠 Classification, Regression, Time Series, Forecasting, Detection & Recognition WHAT I CAN HELP YOU BUILD: ✅ AI & Machine Learning MVPs, SaaS, Startup Predictive models, prototypes, backend APIs, AI model integration, and deployment-ready workflows. ✅ Deep Learning & Computer Vision Solutions Image classification, object detection, segmentation, anomaly detection, medical imaging, and 2D/3D vision pipelines. ✅ LLM, RAG & AI Integration OpenAI API integration, LangChain workflows, ChromaDB knowledge bases, retrieval pipelines, and AI-powered applications. ✅ Model Training & Fine-Tuning Data preparation, training pipelines, experimentation, evaluation, optimization, and reproducible implementation. ✅ Research Support & Code Reproduction Research-paper implementation, GitHub and Hugging Face code reproduction, benchmarking, ablation studies, experiment support, and technical reporting. ✅ Medical & Scientific Data Solutions Research-focused machine learning workflows for structured, image, and multimodal datasets. ✅ Time-Series & Sensor Data Solutions Forecasting, anomaly detection, signal processing, predictive modelling, and machine learning workflows for sensor, IoT, and sequential datasets. CORE TOOLS: Python | PyTorch | TensorFlow/Keras | Scikit-learn | OpenCV | Vercel AI | Github | Hugging Face | Flask | FastAPI | OpenAI API | Claude Code | LangChain | ChromaDB | Next.js | SQL | Docker | R/Rstudio | Matlab PyTorch | TensorFlow | Deep Learning | Neural Networks | Machine Learning | Machine Learning Model | Large Language Model | RAG | AI Model Development | AI Model Integration | Data Engineering | biostatistics | Statistical Analysis | Data Engineering | Image processing | Signal Processing WHAT YOU CAN EXPECT ✓ Clear communication and realistic scoping ✓ Clean, reproducible code and documentation ✓ Research-backed implementation decisions ✓ Flexible collaboration across USA, Europe, UK, and Australia time zones I also support Python/R Data Science Data Analysis, NLP, LLMs, OpenAI API, RAG chatbots, Clinical Data, Data Engineering, scraping tasks projects and build ETL pipelines as well as setup databases. Share your idea, dataset, existing codebase, or research paper, and I will help define the most practical path from prototype to implementation.

Greetings, I am a Senior AI Engineer and Data Architect specializing in Machine Learning, Deep Learning, Federated Learning, LLMs, AI Automation, and Data Architecture. I focus on building scalable, secure, and production-ready AI and data solutions that solve real business problems. My core strength lies in designing end-to-end AI and data systems, from data ingestion, data modeling, and data processing to model development, deployment, automation, and business intelligence. I have strong experience in LLM-based RAG systems, computer vision, privacy-preserving AI using Federated Learning, and modern data platforms. I also work on enhancing AI security through Blockchain integration where applicable. - Key Expertise: * Data Architecture & Engineering * Data Lake, Data Warehouse, and Lakehouse Architecture * Data Modeling: Star Schema, Snowflake Schema * ETL/ELT Pipelines and Data Integration * Data Governance, Data Quality, and Metadata Management * Batch and Real-Time Data Processing * Big Data Platforms: Spark, Hadoop, Cloudera CDP * DataOps, CI/CD, and MLOps - Generative AI and Automation * LLMs, RAG systems, chatbots * AI workflow automation and integrations - Advanced AI * Federated Learning * Secure AI systems with Blockchain integration - Machine Learning * Regression models, Decision Trees, SVM * Ensemble methods: Random Forest, Gradient Boosting, XGBoost * Probabilistic and distance-based models: Naive Bayes, KNN - Deep Learning * ANN, CNN, RNN, LSTM, GAN * Model optimization and deployment - Computer Vision * Image classification, object detection, segmentation I am passionate about collaborating with clients to deliver robust, efficient, and future-ready AI and data solutions. If you are looking for a Senior AI Engineer and Data Architect who combines research-level expertise with real-world implementation, I would be happy to discuss your project.

Do you have an AI vision that needs to become a real, working product? I don't just build models; I engineer complete, scalable solutions that turn data into actionable insights and automation. For over five years, I've specialized in bridging the gap between cutting-edge Artificial Intelligence (AI) research and robust software that delivers real-world value. My core expertise lies in computer vision and machine learning, but my skill set is full-stack. This means I can own your project from the initial data pipeline, through model training and optimization, all the way to deploying a polished desktop application or a secure enterprise API. I thrive on building tools that work seamlessly for end-users, whether it's a retail manager, a traffic controller, or a sports coach. My strongest suit is developing intelligent systems that "see" and understand the world. I've built a retail analytics platform (CrowdIQ) that transforms standard CCTV into a source of business intelligence, tracking customer demographics and behavior. In the sports domain, I created PadelIQ, an analytics engine that uses computer vision to track player movement, posture, and court coverage from match footage, providing real-time coaching feedback. For public safety, I developed a traffic management system (OmniRoad AI) using advanced object detection for real-time accident and congestion monitoring. Beyond computer vision, I architect full-scale data science pipelines. A prime example is my telecom churn prediction project, where I built a machine learning model to identify at-risk customers and paired it with an interactive Power BI dashboard. This end-to-end approach—from data analysis to a clear visualization of insights—ensures the model's findings directly inform business strategy and retention actions. I also develop the tools and infrastructure that power AI applications. I've built secure, enterprise-grade systems like DevelmoGPT, a RAG-based LLM that allows for secure, semantic search over private company documents. From creating simple utilities like PDF-to-audio converters to designing complex role-based access systems, I ensure the foundation of any AI solution is reliable, secure, and maintainable. My process is collaborative and results-driven. I start by deeply understanding your business problem, not just the technical requirement. We'll then iterate through prototyping, development, and testing to ensure the final product not only meets specs but also delivers tangible ROI. I communicate clearly at every stage, providing demos and documentation so you're never in the dark. Let's connect. Share your project idea or challenge, and I'll provide a clear outline of how we can leverage AI, machine learning, or computer vision to build your intelligent solution. Click the invite button to start the conversation. /// The following is just for SEO. You can ignore it /// #computer vision #computer vision engineer #computer vision OpenCV #machine learning computer vision #deep learning computer vision #computer vision machine learning #machine learning python #nlp machine learning

I am an AI Engineer with 4+ years of experience building and deploying production-ready AI systems across classical machine learning, deep learning, computer vision, NLP, and Generative AI. Unlike many AI developers who focus only on LLMs, I work across the entire AI stack. I believe the best solution isn't always a large language model or an expensive API. Many real-world problems are better solved using classical machine learning or deep learning, resulting in lower infrastructure costs, faster inference, reduced latency, and greater control over your solution. My goal is always to build the most effective system not the most expensive one. Some of the areas I regularly work in include: * Classical Machine Learning (XGBoost, LightGBM, CatBoost, Random Forests, SVMs, feature engineering, predictive modelling, forecasting, anomaly detection, recommendation systems) * Deep Learning (PyTorch, TensorFlow, CNNs, Transformers, Vision Transformers, knowledge distillation, model optimization) * Computer Vision (object detection, image classification, segmentation, OCR, document understanding, face recognition, multi-object tracking, embedding-based search) * NLP & LLMs (RAG, GraphRAG, agentic workflows, fine-tuning, embeddings, semantic search, document QA, information extraction) * Generative AI applications using OpenAI, Anthropic, Gemini, and open-source models * End-to-end AI pipelines from data collection and preprocessing to training, evaluation, deployment, and monitoring I also have extensive experience optimizing AI models for production through knowledge distillation, pruning, quantization, and efficient inference, making models smaller, faster, and more cost-effective for both cloud and edge deployments. On the engineering side, I work comfortably with Python, FastAPI, PostgreSQL, pgvector, asynchronous programming, Docker, GPU acceleration, and cloud deployments. I build complete AI products and APIs that are designed to scale not just research prototypes. Beyond implementation, I enjoy solving difficult research and engineering problems. Whether it's designing a predictive model, improving model accuracy, reducing inference costs, building an intelligent document processing pipeline, or deploying an LLM application, I focus on solutions that are reliable, maintainable, and practical for production. I also lead a team of AI engineers, giving me experience not only in technical execution but also in planning, code quality, mentoring, and delivering projects on time. If you're looking for someone who can understand the problem first, choose the right AI approach, and build a production-ready solution that balances performance, cost, and scalability, I'd be happy to help.

-- Top 3% of AI Developers on Upwork (rated: Top Rated Plus) -- 5+ Years of Professional Experience in Machine Learning -- Specialized in Generative AI, Image Generation, and Speech Generation As a specialist in AI, I bring a strong theoretical foundation alongside practical expertise in machine learning, deep learning, and generative AI models. With over 5 years of experience, I excel in applying advanced AI techniques to solve complex problems in image generation, speech synthesis, and more. Key Areas of Expertise: - Deep expertise in generative AI models for image, video, and speech generation (GANs, VAEs, diffusion models). - Skilled in designing and fine-tuning LLMs and multimodal models, integrating image, text, and speech for advanced AI solutions. - Proficient in the full ML/DL pipeline: architecture design, model training, deployment, and continuous optimization. - Extensive experience in applying cutting-edge neural networks for computer vision, NLP, and audio processing. - Developed custom generative models for use cases including text-to-image, image-to-image, speech-to-text, text-to-speech, and voice cloning. - Implemented solutions for automated content generation, image captioning, visual understanding, speech synthesis, and multimodal fusion. - Focused on optimizing models for deployment on mobile, cloud, and edge devices using tools like PyTorch, TensorFlow, and NVIDIA frameworks. Research Experience: - Published work in reinforcement learning at ICML ("Inverse Constrained Reinforcement Learning") with applications in human feedback for improving generative models. - Expertise in Reinforcement Learning from Human Feedback (RLHF), a key technique used in training high-performance LLMs and generative models. Technical Skills: - Core Areas: Generative AI, Image Generation, Speech Generation, Multimodal AI, Computer Vision, NLP, OCR, Large Language Models, Big Data Analytics. - Libraries/Frameworks: TensorFlow, Keras, PyTorch, Hugging Face, OpenAI, CoreML, FastAPI, Flask. - Toolkits: PyTorch, Numpy, Pandas, CUDA, TensorFlow, OpenCV, Tflite. - Operational Tools: GCP, AWS, Kubernetes, Docker, Github, Gitlab.

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.