
I build RAG-powered chatbots, web dashboards, and AI search systems for businesses | Python, OpenAI, Pinecone
၎င်းတို့၏ လင့်ခ်များနှင့် ဤပရိုဖိုင်၏ ကျန်အပိုင်းများကို ကြည့်ရှုခြင်းဖြင့် ၎င်းတို့ မည်သူဖြစ်သည်ကို သိရှိနိုင်ပါသည်။ Zinn Hub သည် Zinner တိုင်း၏ ပရိုဖိုင်ကို ပွင့်လင်းမြင်သာစွာ ထားရှိသည် — ထို့ကြောင့် သင်သည် ယုံကြည်မှုတည်ဆောက်ပြီး ယုံကြည်စိတ်ချစွာ ငှားရမ်းနိုင်ပါသည်။
AKORA သည် ၎င်းတို့၏ ကျွမ်းကျင်မှုများကို Zinn Hub သို့ သက်သေပြခဲ့ပြီး ၎င်းတို့၏ လုပ်ငန်းအထောက်အထားများကို ပေးအပ်ခဲ့သည်။ အတည်ပြုပြီးသော Zinner တိုင်းသည် လက်တွေ့ကျသော အရည်အသွေး အာမခံချက် ပြန်လည်သုံးသပ်မှုကို အောင်မြင်ပြီး — ထို့ကြောင့် သင်သည် ယုံကြည်စိတ်ချစွာ ငှားရမ်းနိုင်ပါသည်။
🛡️ October 2026 မှစ၍ အတည်ပြုပြီးပါပြီ။
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.
ကျွန်ုပ် သင့်အတွက် တည်ဆောက်နိုင်သောအရာများ-
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.
ကျွန်ုပ်၏လုပ်ဆောင်ပုံ-
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.
ဤ freelancer အထူးပြုသည့် နည်းပညာများနှင့် ကျွမ်းကျင်မှုများ။
ဤအလွတ်တန်းသမား ရရှိနိုင်သော ဝန်ဆောင်မှုဧရိယာများ။
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
အသေးစိတ်ကြည့်ရန် →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
အသေးစိတ်ကြည့်ရန် →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
အသေးစိတ်ကြည့်ရန် →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
အသေးစိတ်ကြည့်ရန် →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
အသေးစိတ်ကြည့်ရန် →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
အသေးစိတ်ကြည့်ရန် →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
အသေးစိတ်ကြည့်ရန် →ဤဖရီးလန့်စ်ကို တိုက်ရိုက်စာပို့ရန် အခမဲ့အကောင့်တစ်ခု ဖန်တီးပါ သို့မဟုတ် လော့ဂ်အင်ဝင်ပါ။
ဝင်မည် / အကောင့်ဖွင့်မည်သင်၏ ဒက်ရှ်ဘုတ်များကို ဝင်ရောက်ရန် လက်မှတ်ထိုးဝင်ပါ
ဝင်ရောက်ပါ အကောင့်ဖန်တီးပါသင့်လှည်းတွင် အခြား Zinner တစ်ဦးထံမှ Zinns များ ပါဝင်နေသည်။ သင်သည် တစ်ကြိမ်လျှင် Zinner တစ်ဦးထံမှသာ မှာယူနိုင်ပါသည်။
သင့်လှည်းကို ရှင်းလင်းပြီး ဤ Zinn ကို ထည့်လိုပါသလား၊ သို့မဟုတ် သင့်လက်ရှိ မှာယူမှုဖြင့် ဦးစွာ ငွေရှင်းမလား။
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