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Scraping & Automation Engineer Turn Any Website Into Structured Data I build scrapers, automated workflows, and data pipelines for businesses that need clean, reliable data without the manual work. Whether it's scraping a membership directory, automating a lead funnel, building an AI voice agent, or wiring together a CRM integration, I've built it. What I work with: Python · n8n · Retell AI · Playwright · BeautifulSoup · REST APIs · Webhooks · Google Sheets · CRM Integrations · ETL Pipelines · AI Workflow Automation Typical projects: Web scrapers for product, lead, and listing data Automated workflows that replace manual processes AI voice agents for sales and appointment setting CRM integrations and data pipelines

AI Developer | Automation & Predictive Analytics I build AI systems that take repetitive work off your plate and turn your data into decisions you can act on. Over 5+ years I've automated workflows that ran for hours down to minutes, shipped computer-vision models north of 97.5% accuracy in production, and built specialized models that outperform frontier LLMs on-task at a tiny fraction of the compute. AUTOMATION I design pipelines and AI-driven workflows that replace manual, error-prone processes: LLM-powered document processing, data ingestion and ETL, RAG/MCP integrations, and agentic systems that handle the busywork. I've cut enough of it for clients to gain multiples worth of efficiency gains. PREDICTIVE ANALYTICS I build forecasting, classification, and risk/scoring models that turn historical data into reliable signal - with the feature engineering, evaluation, and monitoring to keep them accurate in production, not just in a notebook. I also handle the engineering around the models - infrastructure, security, and the supporting services that keep everything running in production. When a project calls for it, I work across NLP, computer vision, and custom model training/fine-tuning. SELECTED RESULTS - Automated document processing from hours to minutes with LLMs - 97.5%+ accuracy on production computer-vision inference - Fine-tuned a compact language model to more than 5x the on-task accuracy of a frontier LLM (GPT-5.5) while using thousands of times less compute CORE SKILLS - Predictive analytics & forecasting, machine learning, deep learning, LLMs, computer vision - Workflow & data automation, MLOps, custom model training, optimization & fine-tuning - Supervised, unsupervised (K-Means, HDBSCAN), and reinforcement learning (Stable-Baselines3) STACK - Languages: Python, SQL, Bash - ML/Data: scikit-learn, PyTorch, TensorFlow, XGBoost/LightGBM/CatBoost, pandas - Build & Deploy: FastAPI/Flask, Docker, Kubernetes, Git I care about modular, efficient systems that keep working long after handoff - not one-off scripts that break the moment requirements shift. If you've got a process worth automating or data worth predicting from, let's talk about moving your project forward.

If your data is stuck in the wrong system, in messy files, or behind an awkward API, I can help. I turn complex data problems into working systems and usable deliverables. I help clients move data between platforms, automate manual work, clean up unreliable exports, and build workflows that save time and reduce errors. Whether the deliverable is a program they can keep using, or a clean CSV, Excel file, report, or database, I can make it happen. Most of my work is API integrations, ETL pipelines, backend automation, data processing, and data migrations. I'm at my best when the problem is not clean or perfectly defined. I'll solve that problem first. I build data workflows that are meant to hold up in real use. I work with complex APIs, big JSON or XML data files, elaborate schema, folders full of CSV and Excel files, and large datasets where correctness matters. The result is a cleaner dataset, a dependable program, or a responsive and well-designed workflow. If you need a dependable program, a solid new system, or a clean final dataset, I can help.

I have 8 years of experience in Python development and my skills are listed below: - Web scrapers, bots, automation, web automation, scripting, API scripts, ETL; - Data extraction, data cleaning, data processing, data transformation; - Backend development, web apps with Flask, Django; - Frontend coding with HTML, CSS, Js; - Covering UI with selenium tests.

I am an AI/ML Engineer with 8 years of experience and PhD-trained Bioinformatics Scientist. I specializes in developing production-quality AI systems, ETL/Data Pipeline modeling, deep learning, NLP/LLM workflows, and digitally using AI for pathology. As an experienced hands-on builder of end-to-end AI systems for pharmaceutical research and development (R&D), cancer research, and computational pathology, I have developed AI systems that integrate raw data into production ML application systems from the data pipeline through to deployment, monitoring, and statistical analysis for non-technical stakeholders. I have expertise in both cutting-edge research in the area of machine learning, as well as extensive practical engineering experience with scalable and high reliability ML application systems in production. 📌 Recent Project Experience ✨ Developed ML pipeline to process heterogeneous structured and unstructured data • Pfizer DSDR – Drug Safety AI Pipeline: Built production ML/ETL workflows integrating structured + unstructured data, with API deployment, validation, monitoring, and alerts. ✨ Trained and deployed deep learning networks that can analyze large biomedical data sets • FNLCR – Multi-Modal Pathology Toolkit: Operationalized HALO H&E/mIF whole-slide analysis and million-cell/pixel-scale metadata curation. ✨ Deployed and monitored AI algorithms in production environments with API, monitoring, validation, and cross-functional collaboration • AbbVie Precision Medicine – WSI Modality Prediction: Trained MIL deep learning models with attention heatmaps and Flask API visualization. 💡 What I Can Help You With ✅ Production ML Systems • End-to-end ML pipeline design and implementation • Model training, evaluation, deployment, and monitoring • API-based ML inference systems • Model reliability, robustness testing, and performance tracking ✅ ETL & Data Engineering for AI • Automated ETL pipelines for structured and unstructured datasets • Data validation, error handling, metadata management, and QA workflows • Integration of SQL databases, CSV files, imaging data, and distributed repositories • Scalable data curation pipelines for ML-ready datasets ✅ Deep Learning & Computer Vision • PyTorch/Keras model development • Vision transformers, foundation models, CNNs, MIL models, segmentation models • Image classification, prediction, feature extraction, and attention visualization • Large-scale image analysis for gigapixel whole-slide images ✅ Digital Pathology & Biomedical AI • Whole-slide image analysis for H&E, IHC, and multiplex immunofluorescence • Weakly supervised multi-instance learning for histopathology • Spatial omics, single-cell analysis, graph-based modeling, and image-derived biomarkers • HALO-based image analysis workflows and algorithmic scoring exports ✅ NLP/LLM & AI Workflow Integration • LLM-powered data processing and knowledge extraction workflows • Biomedical text/data integration • AI-assisted research pipelines and automation • Foundation model evaluation and applied AI system development ⚙ Technical Expertise Languages: Python(Advanced), SQL(PostgreSQL, MS SQL Server), R, Go AI/ML/DL: PyTorch, Keras, Statistical ML, Computer vision, MIL, Transformers, DNN, CNN NLP/LLM: RAG, LangChain, LangGraph LlamaIndex, OpenAI API, Anthropic Claude, Hugging Face, AutoGPT, AgentGPT Data/ETL: CSV, Structured/unstructured data integration, Metadata pipelines, Pandas, Numpy, Data validation Frontend: React, Next.js, TypeScript, HTML5, CSS3, Tailwind CSS, Material-UI Backend: Node.js, Express.js, GraphQL Databases: MongoDB, MySQL, Redis, Firebase Deployment: Flask, REST APIs, Linux/Unix, Git, Production ML workflows E-Commerce: Shopify (Liquid, Apps, Plus), WordPress, WooCommerce, Elementor DevOps: AWS, Docker, CI/CD, Nginx, PM2 Automation: n8n, Make, Zapier, Temporal, Apache Airflow CRM/Marketing: HubSpot, GoHighLevel, Salesforce, ActiveCampaign Data Visualization: Power BI, Looker Studio, Matplotlib, Plotly Domains: Digital pathology, Big Data, Statistical testing, Spatial Omics, Biomedical AI, Pharmaceutical R&D, Cancer research 🤝 Why Work With Me? ✓ PhD-level AI/ML expertise with real-world production experience ✓ Strong ability to bridge research, engineering, and domain science ✓ Experienced in working with pharmaceutical, biomedical, and cross-functional teams ✓ Clean, scalable, well-documented code and reproducible workflows ✓ Strong communication and attention to detail for complex technical projects I am particularly suited to taking on clients who require more than a typical ML developer, specifically those involved with highly complex biomedical data, multi-dimensional imaging, production ML systems and AI workflows that are built from research through to deployment. If you need assistance with creating an effective AI pipeline, deploying an ML model, developing a CV system, or analyzing digital pathology data, I would be glad to work with you on your project.

Hi, I am Md. Redwan Islam, a Computer Science graduate researcher at the University of Georgia with strong experience in machine learning, artificial intelligence, knowledge graphs, graph neural networks, LLM-based workflows, IoT systems, cloud-based analytics, and research-oriented software development. My recent work focuses on applied AI/ML research and implementation, especially graph-based AI, dynamic graph neural networks, knowledge graph construction, biomedical knowledge representation, explainable AI, and LLM-assisted research systems. I have worked on projects involving biologically inspired framework for scalable and adaptive graph neural networks on various large datasets. I also work on knowledge graph-based biomedical AI, including antimicrobial resistance, horizontal gene transfer, mobile genetic elements, and scientific data integration from heterogeneous biological databases. My experience includes building structured pipelines for data collection, entity-relation modeling, graph construction, semantic representation, graph analytics, and machine learning over complex scientific datasets. In addition to graph AI and knowledge graphs, I have experience with LLM applications, retrieval-augmented generation concepts, AI research automation, question-answering systems, literature-based reasoning, prompt engineering, and integrating LLMs with structured data sources. I can help design AI workflows that connect plain-language questions to databases, knowledge graphs, APIs, or analytical pipelines. At the University of Georgia, I have worked as a Graduate Research Assistant and Graduate Teaching Assistant, supporting courses such as Data Mining, Discrete Mathematics, and Data Science. My academic and research background includes machine learning, deep learning, data mining, signal processing, public health analytics, IoT-based real-time data collection, and cloud-based data analysis. I have also contributed to research involving NHANES data analysis, explainable public health analytics, Raman spectroscopy with machine learning, and brain-computer interface signal classification. Before joining UGA, I completed my B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology. During my undergraduate research, I published an IEEE conference paper on electrocorticography-based motor imagery signal classification using continuous wavelet transform. I later worked as an IoT Software Developer at DataSoft Systems Bangladesh and as a Satellite Operation / Computer and Data Center Engineer at Spectra International Limited, where I contributed to the Bangabandhu Satellite-1 project with Thales Alenia Space, France. That work involved server installation, application software maintenance, networking equipment, switches, routers, and data center infrastructure. I can help with: Machine Learning and Deep Learning Graph Neural Networks and Graph Analytics Knowledge Graph Construction and KG-Based AI LLM Applications and RAG-Style Workflows Biomedical and Scientific AI Pipelines Python Data Analysis and Research Prototyping NLP, Data Mining, and Text Analytics IoT Data Collection and Cloud-Based Analytics Database Design, APIs, and Backend Development Academic Research Coding, Experimentation, and Paper-Ready Results My technical stack includes Python, PyTorch, TensorFlow, Keras, scikit-learn, NumPy, SciPy, Pandas, R, MATLAB, Java, C/C++, C#, JavaScript, Node.js, REST APIs, Django, Laravel, SQL, MySQL, MongoDB, Linux, Docker, Git, GitHub, AWS, Azure, SPSS, LaTeX, and data visualization tools. I am especially interested in projects where AI research needs to be turned into a working prototype, reproducible codebase, analytical pipeline, technical report, or production-ready proof of concept. If your project involves machine learning, knowledge graphs, LLMs, graph data, scientific datasets, biomedical AI, or research-driven software development, I can help you build it carefully, clearly, and rigorously.