Hire the Best Feedforward 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.

With 5+ years across Computer Vision, NLP, and full-stack AI development, I've delivered: 🎯 Computer Vision & Object Detection — Real-time YOLOv8/v9 pipelines with 96.8% accuracy (passenger detection system) — Parking occupancy detection, multi-camera feed processing, ROI-based classification — Medical image AI: 96% pneumonia detection, 94% brain tumor classification — OpenCV, image segmentation, hyperspectral imaging (from MSCS research) 🤖 NLP, LLMs & RAG Systems — RAG-based chatbots with real-time database integration — Healthcare NLP: 95% F1-score for medical entity extraction — AI Purchase Agent with reorder forecasting & supplier comparison (5★ client review) — GPT, BERT, LangChain, vector databases, semantic search 📊 Data Science & Fraud Detection — Real-time fraud detection: 99.2% accuracy at 10,000+ transactions/minute — ML pipelines on Azure, MLflow experiment tracking — XGBoost, ResNet, CNN, TensorFlow, PyTorch ⚙️ Backend & Deployment — FastAPI, Flask, Docker, Apache Kafka — Azure cloud deployment, SQL Server — Clean, documented, production-grade Python code I hold an MSCS in Deep Learning from GIK Institute and a BSc in AI/ML from Bahria University, and I'm currently Senior Data Scientist at SeethruTech. → Available 30+ hrs/week | 0–4 hr response | 100% delivery rate → Let's discuss your project — I respond within 2 hours.

🟢 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 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.

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

Research, development and consulting in projects involving Artificial Intelligence, Neural networks, Statistics, Computer vision, Signal and Image processing, Operation research, and Algorithm design. Ph.D. in Computer Science and Applied Mathematics