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I build AI agents and LLM systems in Python that hold up in production, not just in a demo. I have a PhD and about ten years as a data scientist behind that work. Top Rated here, with 4,000+ hours. The hard part of this work is reliability, and that is where I am strongest. If you have tried this already, you know the failure modes. An agent that works in a demo and breaks on real inputs. An LLM that answers confidently and wrong. A prototype nobody trusts enough to ship. I spent years on those same problems before LLMs, in forecasting and trading systems, where a model that is quietly wrong costs real money. That is why I think about agents the way I do. What I do: AI agents and LLM workflows. Tool calling, orchestration across APIs, and the parts that decide whether it survives contact with reality: input validation, retries, and knowing when to hand control back to deterministic code or to a person. RAG and document intelligence. Retrieval, reranking, and answers that cite their source, for the cases where "probably right" is not good enough. The engineering around it. Python and FastAPI, the Claude and OpenAI APIs, vector databases, deployed on AWS, GCP, or Azure. I will be honest about fit. I am a data scientist who moved into AI engineering, not someone whose only experience is calling an API. If your problem is mostly about reliability, accuracy, and ending up with a system your team can maintain, that is where I add the most. I recently spent several months reviewing a production LLM training pipeline, so I have seen how these models behave across far more inputs than any single build shows. Tell me what you are building and what it has to get right, and I will give you an honest read on how I would approach it and whether I am the person for it. Stack: Python, FastAPI, Claude API, OpenAI API, LangChain/LangGraph, RAG, pgvector/Pinecone/Qdrant, PostgreSQL, Docker, AWS/GCP/Azure.

Most businesses are sitting on valuable data, they just don't know how to use it yet. I help founders, managers, and growing businesses turn that data into clear decisions, accurate predictions, and real results. I work with businesses to solve practical problems using data: forecasting outcomes, understanding customer behavior, automating repetitive analysis, and building intelligent systems that save time and reduce guesswork. - Turning messy, overwhelming data into clear, actionable insights - Building prediction models with 87%+ accuracy that help you plan ahead with confidence - Automating manual reporting and analysis to save your team hours - Identifying patterns in customer data to support smarter business decisions - Delivering results you can actually act on, not just technical reports A few things I'm proud of: - ⭐ 100% Job Success Score across all completed projects on Upwork - 🎯 Built prediction models achieving 87% accuracy on real-world data - ✅ 5-star reviews on every completed client engagement across multiple platforms - 📊 Delivered solutions across healthcare, education, and business analytics I focus on practical solutions, not just theory. Every project I take on is built around your specific business goals and designed to fit into how your team already works, no unnecessary complexity, just results. If you're ready to make your data work for you, I'd love to hear about your project. Let's talk.

Hi! I’m a 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝘁 with 4+ years of experience helping companies turn raw data into clear predictions, insights, and measurable results. I work across 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲, 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝘀𝗽𝗼𝗿𝘁𝘀 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗡𝗕𝗔 & 𝗡𝗙𝗟), 𝗮𝗻𝗱 𝘀𝗼𝗰𝗶𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁, delivering models that actually perform in the real world. 𝗔 𝗿𝗲𝗰𝗲𝗻𝘁 𝗰𝗹𝗶𝗲𝗻𝘁 𝘀𝗮𝗶𝗱 𝗶𝘁 𝗯𝗲𝘀𝘁: ⭐⭐⭐⭐⭐ “I've worked with Ahmed on a number of projects and he continues to do an excellent job. The project was completed successfully and another one is already being started. He is a great freelancer to work with” 🚀 𝗪𝗵𝗮𝘁 𝗜 𝗛𝗲𝗹𝗽 𝗪𝗶𝘁𝗵 • Predictive modeling & forecasting • Sports analytics (NBA/NFL DFS) • NLP & text classification • Clustering, segmentation & ML pipelines • Data analytics & dashboarding I focus on solutions that are practical, accurate, and actionable - not academic experiments. 📊 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 • Built forecasting models with 85%+ accuracy • Increased sales by 50% through smarter data pipelines • Saved $150,000/year with simulation-based optimization • Automated processes that cut payment cycles from 90 to 30 days I’ve also worked with UNDP, Save the Children, and Omdena, applying ML to global connectivity, solar adoption, and online child safety. 🤝 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗛𝗶𝗿𝗲 𝗠𝗲 • Fast, reliable communication • Business-minded problem solving • Models that deliver real impact • A versatile skillset across industries 🛠 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 Python • SQL • Pandas • NumPy • Scikit-Learn • TensorFlow • NLP • BigQuery • Looker Studio • Google Cloud • Tableau • ML pipelines • Forecasting • Clustering • CV models. 📞 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘂𝗻𝗹𝗼𝗰𝗸 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮? Send me a message - I’d love to help you turn your dataset into something valuable.

I’m a Machine Learning Engineer who builds end-to-end prediction systems from real-time and streaming models to offline batch pipelines that power mission-critical prediction applications. Alongside traditional ML, I work on next-gen AI solutions including Generative AI, intelligent agents workflows, and retrieval-augmented systems, combining MLOps best practices with cloud native infrastructure to deliver scalable, production ready AI. Ultimately, I'm passionate about engineering next-gen AI that delivers tangible business value and scales for real-world impact.

🌟🌟🌟 Experienced Software Engineer with a proven track record in developing sophisticated systems across diverse industries. 🌟🌟🌟 ✅ Proficient in creating robust models for stock trading, medical equipment detection, anomaly detection, and object classification using PyTorch, TensorFlow, and Ray. ✅ Extended experience setting up CI/CD pipelines and cloud deployment strategies. ✅ Enthusiastic about web development, focusing on Django, .NET Core, and Laravel frameworks and front-end development with React.js. ✅ Keen interest in Crypto/Blockchain development, exploring the innovative possibilities in this exciting field.

Helping people to make sense of data is my personal mission, which involves advocating a data-driven culture. As a Data Scientist, I have nearly 10 years of experience in extracting, cleansing/wrangling data, conducting Exploratory Data Analysis (EDA), building Machine Learning and Deep Learning models, and presenting insights to stakeholders through effective data visualization. Completed projects include building Machine Learning models to predict housing prices, detect financial criminal activity, assign sentiment scores in financial report and perform customer segmentation.