
I build RAG-powered chatbots, web dashboards, and AI search systems for businesses | Python, OpenAI, Pinecone
Prozkoumejte jejich odkazy a zbytek tohoto profilu, abyste zjistili, kdo jsou. Zinn Hub udržuje profil každého Zinnera otevřený a transparentní – takže si můžete vybudovat důvěru a najímat s jistotou.
AKORA prokázal své dovednosti Zinn Hub a poskytl důkazy o své práci. Každý ověřený Zinner prochází praktickou kontrolou kvality – takže si můžete najmout s důvěrou.
🛡️ Ověřeno od October 2026
I am an AI Engineer who builds intelligent systems that help businesses unlock the value hidden inside their own data. My specialty is Retrieval-Augmented Generation (RAG) — the technology that transforms static documents, PDFs, and knowledge bases into interactive, AI-powered assistants that answer questions in plain English.
I discovered my passion for AI engineering through an intensive, hands-on scholarship program where I completed a comprehensive track covering the entire modern AI stack. Through this journey, I built multiple working systems from scratch — not just tutorials, but real, functional applications. I learned how to architect RAG pipelines, manage vector databases, engineer effective prompts, and deploy AI tools that solve actual problems.
Co pro vás mohu vytvořit:
RAG-Powered Chatbots: AI assistants that read your documents and answer customer or employee questions accurately, with citations to the original source.
Semantic Search Engines: Search tools that find information by meaning, not just keywords — so your team finds what they need even when they don't know the exact words.
Document Q&A Systems: Turn your PDFs, manuals, and knowledge bases into an instant-answer system that saves hours of manual searching.
Interactive AI Apps: User-friendly Streamlit applications that make your AI tools accessible to anyone, no technical skills required.
Data Processing Pipelines: Automated scripts that clean, batch, and prepare your data for AI systems using Pandas and NumPy.
My Technical Toolkit:
Python, Pandas, NumPy, OpenAI API (Embeddings, Chat Completions, Function Calling, Responses API), Pinecone, ChromaDB, LangChain, Streamlit, t-SNE, Matplotlib, Git, and REST API integration.
Jak pracuji:
I believe in clean, working solutions — not overcomplicated theory. I communicate clearly, deliver on time, and focus on understanding your actual problem before writing a single line of code. Whether you need a small prototype to test an idea or a complete system to automate a workflow, I will work with you to build something that genuinely helps.
I am early in my freelance journey, which means you get dedicated attention, competitive rates, and a genuine commitment to your project's success. I am not here to collect a paycheck — I am here to build a reputation, one happy client at a time.
If you have a document-heavy workflow, a customer support challenge, or an idea for an AI tool, let's talk. Send me a message and tell me what you're trying to solve. I will tell you honestly whether I can help — and if I can, I will give it everything I have.
Technologie a odborné znalosti, na které se tento freelancer specializuje.
Oblasti služeb, pro které je tento freelancer k dispozici.
Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib
Zobrazit podrobnosti →Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib
Zobrazit podrobnosti →Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib
Zobrazit podrobnosti →Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib
Zobrazit podrobnosti →Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib
Zobrazit podrobnosti →Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib
Zobrazit podrobnosti →Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib
Zobrazit podrobnosti →Vytvořte si bezplatný účet nebo se přihlaste a pošlete zprávu tomuto freelancerovi přímo.
Přihlásit se / Zaregistrovat seVáš košík obsahuje Zinns od jiného Zinnera. Objednávat můžete pouze od jednoho Zinnera najednou.
Chcete vyprázdnit košík a přidat tento Zinn, nebo nejprve dokončit aktuální objednávku?
Získejte aplikaci Zinn Hub
Oznámení · Rychlejší přístup · Celá obrazovka
Klepněte na Sdílet ve vašem prohlížeči
➜ Poté klepněte na "Přidat na plochu"