Hire the Best Artificial Neural Network Specialists

About 7 years of architecting data-driven enterprise-grade solutions for top-tier corporations, I've now strategically transitioned to freelancing and contract-based roles. My mission? To bring that production-level expertise, spanning classical ML, deep learning, NLP, MLOps, and the cutting edge of LLMs directly to forward-thinking businesses ready to redefine their capabilities. Here’s a snapshot of the technologies I bring to the table: - Classical Machine Learning: Proficient in a wide array of traditional ML algorithms, including K-means, Random Forest, Logistic Regression, Support Vector Machines (SVMs), Gradient Boosting (XGBoost, LightGBM), and more, ensuring robust predictive modeling and insightful data analysis. - Deep Learning & Neural Networks: My expertise extends to advanced deep learning architectures, encompassing Convolutional Neural Networks (CNNs) for computer vision, Recurrent Neural Networks (RNNs), and Transformers. I leverage frameworks like PyTorch and TensorFlow to build and deploy sophisticated deep learning models. - Natural Language Processing (NLP): From fundamental text processing to cutting-edge language understanding, I specialize in NLP techniques, including Named Entity Recognition (NER), sentiment analysis, text summarization, topic modeling, and advanced language generation. I'm particularly adept at working with Hugging Face transformers and various NLP libraries. - Large Language Models (LLMs) & Generative AI: I have hands-on experience in implementing and fine-tuning Large Language Models (LLMs), including working with OpenAI API for custom solutions. My capabilities include developing and deploying Generative AI pipelines for tasks like RAG (Retrieval Augmented Generation), Stable Diffusion, Text-to-Speech, Image Segmentation, and advanced content creation. - MLOps: Beyond model development, I understand the critical importance of operationalizing AI. I have experience with MLOps practices for seamless deployment, monitoring, and management of machine learning models in production environments, ensuring scalability and reliability. I'm highly skilled in essential programming languages like Python (Pandas, Scikit-learn, NumPy) and R, alongside SQL and SAS, to build, analyze, and optimize these systems. I've optimized ML/AI systems right down to the GPU programming level, ensuring maximum performance and efficiency for compute-intensive workloads.

AI & Machine Learning Engineer | NLP | Generative AI | LLMs | Prompt Engineering | Data Science I help businesses build intelligent systems that work — at scale, in production, and with measurable results. With 3+ years of hands-on experience as an ML and AI Engineer, I specialize in: ✅ Machine Learning & Predictive Modeling — Building and deploying ML models using Python, TensorFlow, Scikit-learn, and PyTorch for regression, classification, forecasting, and recommendation systems. ✅ Generative AI & LLMs — Developing RAG pipelines, AI chatbots, and custom LLM applications using OpenAI GPT, LangChain, and Hugging Face Transformers. Fine-tuning models for domain-specific tasks. ✅ NLP & Text Analytics — Sentiment analysis, topic modeling, text classification, named entity recognition (NER), and document processing pipelines. ✅ AI Engineering & MLOps — End-to-end AI system design, REST API development with FastAPI/Flask, model deployment on AWS/Azure/GCP, and CI/CD for ML pipelines. ✅ Prompt Engineering — Crafting optimized prompts for GPT-4, Claude, and other LLMs to maximize accuracy, relevance, and brand alignment for business applications. ✅ Computer Vision — Object detection (YOLO), image segmentation, OCR, and real-time video analytics systems. ✅ Data Analytics & Visualization — Power BI dashboards, SQL-based data pipelines, and actionable business intelligence reports. Tech Stack: Python | TensorFlow | PyTorch | Scikit-learn | LangChain | OpenAI API | Hugging Face | FastAPI | Flask | AWS | Azure | SQL | Power BI | Docker I hold a PhD in Data Science (University of Canterbury) and an MPhil in Computer Science (Quaid-e-Azam University), plus certifications from DeepLearning.AI and AWS. Whether you need an ML model built from scratch, an AI chatbot integrated into your product, or a full generative AI pipeline — I deliver production-ready solutions, not just experiments. Let's build something intelligent together.

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.

Senior AI Engineer | Generative AI | Full Stack ML Systems | YOLO Expert | MLOps I’m a specialized AI/ML engineer with over 3 years of hands-on experience designing and deploying end-to-end machine learning systems — from custom LLM pipelines and vision models to scalable backend integrations and autonomous AI agents. I work at the intersection of deep learning, production-ready engineering, and AI-driven product development. Specialties: Computer Vision & Object Detection • Full expertise across all YOLO variants: YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-NAS • Custom training with annotated datasets (COCO, Pascal VOC, custom formats) • Model compression, quantization, ONNX/TensorRT export for edge deployment • Real-time inference APIs, multi-object tracking (DeepSORT, ByteTrack) • Medical and industrial use-cases (e.g., diagnostics, defect detection) LLMs & Generative AI • Local + API-based LLM integration: OpenAI, LLaMA, Mistral, Falcon, GPT-J • RAG architecture using FAISS, Chroma, Weaviate, Qdrant • LangChain agent chains: tool use, memory, routing, and personalization • Multi-modal pipelines: text + image + document reasoning MLOps & Deployment • FastAPI, Docker, TorchServe, BentoML for scalable deployment • Model optimization: pruning, quantization, batching • GPU-accelerated workloads (AWS, Lambda Labs, GCP) • CI/CD pipelines for reproducible ML development Full Stack AI Engineering • Frontend: React, Next.js, Tailwind • Backend: FastAPI, Node.js, RESTful + WebSocket APIs • Databases: PostgreSQL, MongoDB, Redis • Autonomous agents with Playwright, ScrapeGraphAI, Selenium, LangGraph Project Highlights: • YOLOv11-based Smart Surveillance: Deployed real-time detection + tracking for multi-class scenarios with alerting pipeline and frontend dashboard. • Medical VQA & Reporting: Created a multi-modal system that extracts diagnostic details from X-rays + generates detailed reports using VQA + LLMs. • AI Search Agent: Built an autonomous search bot using LLMs + real-time scraping with memory and historical context integration. • Document Generation Platform: Custom-built platform using local LLMs to generate reports, contracts, and structured documents with fine control. Why Hire Me? • Expert in both research-level ML and scalable production systems • Proven experience with high-impact, real-world AI projects • Focus on clean code, optimization, and long-term maintainability • Strong communicator who aligns deliverables with your business goals Let’s build something advanced. Drop a message — I respond fast and speak your tech language.

🟢 Available Now Ready to collaborate 24/7 — I’m a full-time freelancer on Upwork. Let’s Take Your Business to the Next Level Building AI solutions should feel innovative, not overwhelming. For the past 3+ years, I’ve worked on solving real-world problems through Artificial Intelligence, transforming raw data into intelligent systems that create meaningful impact. I specialize in Machine Learning, Deep Learning, Computer Vision, NLP, LLM applications, and Predictive Analytics—building solutions that move beyond experimentation and focus on real-world implementation. 𝗛𝗼𝘄 𝗜 𝗰𝗿𝗲𝗮𝘁𝗲 𝗶𝗺𝗽𝗮𝗰𝘁: 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Developed Stress Detection, Anxiety Detection, and Depression Detection systems using facial analysis, Action Units, video processing, and deep learning techniques. 𝗡𝗟𝗣 & 𝗟𝗟𝗠 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Exploring chatbot development, AI automation, text processing, and LLM-powered applications. 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: From data preprocessing and feature engineering to model training, optimization, deployment, and scalable AI workflows. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Python • Machine Learning • Deep Learning • PyTorch • TensorFlow • OpenCV • YOLO • NLP • LLM Applications • Data Analysis • Predictive Analytics I enjoy building AI systems that solve complex challenges in healthcare, finance, automation, and intelligent applications. Always open to discussing AI projects, collaborations, and innovative ideas.

I'm a Junior Artificial Intelligence Engineer with a strong foundation in Machine Learning, Deep Learning, Natural Language Processing (NLP), and Computer Vision. I am committed to transforming data into intelligent solutions that address real-world challenges. In my early career, I've gained valuable experience working on a range of projects, from developing diagnostic tools for medical imaging to creating NLP models for language translation and sentiment analysis. My focus is on building and contributing to AI systems that solve complex problems and drive innovation. My technical expertise includes: Machine Learning: Skilled in building predictive models, optimizing algorithms, and deploying scalable solutions. Deep Learning: Proficient in designing and training neural networks for tasks like image recognition, object detection, and speech processing. Natural Language Processing (NLP): Experienced in developing models for text classification, translation, sentiment analysis, and more. Computer Vision: Expert in image processing, image recognition, image segmentation, and object detection with hands-on experience in using YOLO, TensorFlow, and OpenCV. Whether you're looking to develop a cutting-edge AI application, enhance your existing systems, or explore new AI-driven opportunities, I'm here to help. I'm dedicated to delivering high-quality, impactful solutions that align with your goals. Let’s connect and explore how I can contribute to your next project!