Hire the Best Artificial Neural Networks Experts

🌟 Top Rated Plus AI Engineer | PhD in Computer Science (AI, ML & Generative AI) I specialize in transforming complex ideas into scalable, real-world AI solutions that deliver measurable impact. With a strong blend of research excellence and practical implementation, I help businesses and researchers build intelligent systems that actually work in production. 🧠 Core Expertise 🔹 Computer Vision (YOLO, Vision Transformers, Object Detection) 🔹 Deep Learning (CNNs, Transformers, Vision Transformers - ViTs) 🔹 Generative AI & Large Language Models (LLMs) 🔹 NLP & Fine-tuning (BERT, LLaMA) 🔹 Predictive Modeling & Data Science 🔹Model Optimization & Deployment 💼 Featured Projects 🚧 AI-based Helmet Detection System (YOLOv8 + Vision Transformers) 🌱 Plant Disease Classification using Deep Learning 📉 Customer Churn Prediction System 🧾 Urdu NLP & LLM Fine-tuning Solutions 🏥 Medical Imaging with Explainable AI (Grad-CAM) 🎯 What I Can Do for You ✔ Design and develop custom AI/ML solutions tailored to your business ✔ Build Computer Vision systems (detection, classification, segmentation) ✔ Fine-tune and deploy LLMs & Generative AI applications ✔ Convert research papers into working, production-ready models ✔ Optimize models for performance, scalability, and deployment 🛠️ Tech Stack 💻 PyTorch | TensorFlow | OpenCV | Transformers | Python | Scikit-learn 💡 Why Choose Me? ✨ Top Rated Plus freelancer with a proven track record ✨ Strong PhD-level research + industry implementation expertise ✨ Clear communication, reliability, and on-time delivery ✨ Focus on building accurate, efficient, and production-ready AI systems 📩 Let’s collaborate to bring your AI idea to life! If you’re looking for a dependable expert in AI, Machine Learning, or Generative AI, I’d be happy to discuss your project. Regards 𝑫𝒓. 𝑺𝒂𝒏𝒂 𝑪𝒉𝒆𝒆𝒎𝒂

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)

Top 1% Expert-Vetted Freelancer. I have a PhD in Computer Science (AI & Machine Learning) and I am a physician. I build custom AI systems with proprietary and public data, including creation of novel datasets and state-of-the-art models. * After a 30-minute to 1-hour initial meeting, I'll craft a clearly written report outlining a unique AI strategy for your project. * For long-term engagements, I implement AI solutions end-to-end, including data collection, data cleaning, novel model design, model implementation and training in Python, and iteration on the modeling and data strategy to achieve excellent performance. * One of my specialties is creating custom models for proprietary datasets, including images, videos, audio recordings, and domain-specific data. Book a 30-minute consultation with me to get immediate answers about your AI project: - How can you leverage AI in your organization? - What kind of AI do you need to solve a particular problem? - Should you use a model like ChatGPT/Claude or do you need a custom model? - What modeling strategy should you use? - How should you collect and annotate data? - How much data will you need? - How should you measure performance? After a consultation I’ll craft a clearly-written report outlining an AI strategy for your project. Specialties: Healthcare AI: I am an expert in healthcare AI, with a unique background that blends clinical understanding from my MD, deep technical expertise from my AI PhD, and business experience. I have completed 100+ engagements across medical specialties including radiology, dermatology, cardiology, mental health, family medicine, surgery, dentistry, physical therapy, women’s health, pediatrics, nutrition, and neurology. I have created datasets and custom models for diverse forms of biomedical data including x-rays, CTs, MRIs, clinical photographs, patient videos, medical audio, medical notes, EHR data, insurance claims, omics data, and more. Computer Vision: I create custom AI models for 2D images, 3D images, and videos on proprietary datasets. I am excited about projects from multiple industries including healthcare, manufacturing, automotive, and agriculture. I have extensive experience with classification, object detection, segmentation, and keypoint detection, to identify and localize abnormalities or features of interest. I developed the first machine learning model in the world to predict multiple abnormalities simultaneously from a CT scan. For a dermatology practice, I built the first CV model to predict Fitzpatrick skin type, pigmentation, redness, and wrinkle severity (mean accuracy 85%)—now a core model at Kesty AI. Artificial Intelligence R&D: I have led multiple AI research projects across industry and academia. I have developed novel AI methods, including HiResCAM, a convolutional neural network explanation method with mathematical guarantees. I have published original research across multiple areas of AI, including computer vision, natural language processing, explainable AI, expert systems, and applied AI. My research papers have been cited over 1,000 times. My healthcare AI blog Glass Box has over 700,000 readers from 185 countries. Natural Language Processing (NLP): I have a deep understanding of large language models (LLMs) like Claude, Gemini, ChatGPT, and Llama. I have leveraged Transformers and other NLP techniques for numerous applications, including customized chatbots, medical note generation, and structured information extraction. Advising Entrepreneurs: Before focusing on AI consulting, I spent seven years as the founder of a health AI startup. I led my previous company from concept to deployed B2B SaaS product serving medical practices. Our AI history-taking assistant and AI scribe saved clinicians 2+ hours daily. I managed engineering teams of 5-10 (60+ contributors), secured two U.S. patents, and raised competitive grant funding. I enjoy working with entrepreneurs and discussing pitch decks, fundraising, customer discovery, designing an MVP, and evaluating the ROI of an AI product. If you’d like to talk with me about your AI project, please feel free to send me a message or book a consultation using the link on my profile.

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.

I am an AI engineer with 4 years of experience building and deploying production ready AI systems across computer vision, NLP, and generative AI. I have worked on everything clients need right now: fine tuning and training deep learning models, building RAG and GraphRAG pipelines, LLM powered applications, OCR and document extraction, real time face recognition and multi object tracking, classification systems, embedding pipelines, and end to end data workflows from raw input to deployed output. I also have experience with model optimization including distillation, pruning, and quantization for edge and cloud deployment. On the engineering side I am comfortable with FastAPI, PostgreSQL, pgvector, Python async, and cloud and GPU based deployments. I have built and shipped full stack AI products, not just models. I also lead a team of AI engineers, so I understand both deep technical execution and what it takes to deliver consistently on real projects. If you have an AI problem that needs to actually work in production, I can build it.

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.