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ನಿಮ್ಮ ಕಾರ್ಟ್
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AKORA

I build RAG-powered chatbots, web dashboards, and AI search systems for businesses | Python, OpenAI, Pinecone

📍 Located in: ಉಗಾಂಡಾ 🕐
🎯🤝 ಒಂದು-ಬಾರಿ ಅಥವಾ ದೀರ್ಘಾವಧಿಯ ಕೆಲಸಕ್ಕೆ ಮುಕ್ತವಾಗಿದೆ 🌐 ನಿಮ್ಮ ಸಮಯ ವಲಯದಲ್ಲಿ ಕೆಲಸ ಮಾಡಬಹುದು
👤 ಫ್ರೀಲ್ಯಾನ್ಸರ್
✔ ಪರಿಶೀಲಿಸಲಾಗಿದೆ
⭐ Zinner ಮಟ್ಟ 1
🔗 AKORA ಆನ್‌ಲೈನ್‌ನಲ್ಲಿ ಹುಡುಕಿ
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ಅವರು ಯಾರೆಂದು ತಿಳಿದುಕೊಳ್ಳಲು ಅವರ ಲಿಂಕ್‌ಗಳು ಮತ್ತು ಈ ಪ್ರೊಫೈಲ್‌ನ ಉಳಿದ ಭಾಗವನ್ನು ಅನ್ವೇಷಿಸಿ. Zinn Hub ಪ್ರತಿ Zinner ನ ಪ್ರೊಫೈಲ್ ಅನ್ನು ಮುಕ್ತ ಮತ್ತು ಪಾರದರ್ಶಕವಾಗಿ ಇರಿಸುತ್ತದೆ — ಆದ್ದರಿಂದ ನೀವು ನಂಬಿಕೆಯನ್ನು ಬೆಳೆಸಿಕೊಳ್ಳಬಹುದು ಮತ್ತು ವಿಶ್ವಾಸದಿಂದ ನೇಮಿಸಿಕೊಳ್ಳಬಹುದು.

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Zinn Hub ನಿಂದ ಪರಿಶೀಲಿಸಲಾದ ಕೌಶಲ್ಯಗಳು

ಪರಿಶೀಲಿಸಿದ Zinner

AKORA Zinn Hub ಗೆ ತಮ್ಮ ಕೌಶಲ್ಯವನ್ನು ಸಾಬೀತುಪಡಿಸಿದ್ದಾರೆ ಮತ್ತು ಅವರ ಕೆಲಸದ ಪುರಾವೆಗಳನ್ನು ಒದಗಿಸಿದ್ದಾರೆ. ಪ್ರತಿ ಪರಿಶೀಲಿಸಿದ Zinner ಕೈಯಿಂದ ಗುಣಮಟ್ಟದ ಭರವಸೆ ಪರಿಶೀಲನೆಯನ್ನು ಪಾಸ್ ಮಾಡುತ್ತಾರೆ — ಆದ್ದರಿಂದ ನೀವು ವಿಶ್ವಾಸದಿಂದ ನೇಮಿಸಿಕೊಳ್ಳಬಹುದು.

🛡️ October 2026ರಿಂದ ಪರಿಶೀಲಿಸಲಾಗಿದೆ

ಸದಸ್ಯರಾದಾಗಿನಿಂದ
Oct 2026
Zinns ಪಟ್ಟಿಮಾಡಲಾಗಿದೆ
0
ಕೊನೆಯ ಸಕ್ರಿಯ
18 hours ಹಿಂದೆ

ಕುರಿತು

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.

ನಾನು ನಿಮಗಾಗಿ ಏನನ್ನು ನಿರ್ಮಿಸಬಹುದು:

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.

ನಾನು ಹೇಗೆ ಕೆಲಸ ಮಾಡುತ್ತೇನೆ:

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.

ಕೌಶಲ್ಯಗಳು

ಈ ಫ್ರೀಲ್ಯಾನ್ಸರ್ ಪರಿಣತಿ ಹೊಂದಿರುವ ತಂತ್ರಜ್ಞಾನಗಳು ಮತ್ತು ಪರಿಣತಿ

Search Clicks— AI ಹುಡುಕಾಟದ ಮೂಲಕ ಅದನ್ನು ಕಂಡುಕೊಂಡ ನಂತರ ಖರೀದಿದಾರರು ನಿಮ್ಮ Zinns ನಲ್ಲಿ ಕ್ಲಿಕ್ ಮಾಡಿದ ಒಟ್ಟು ಸಂಖ್ಯೆ.

ವರ್ಗಗಳು

ಈ ಫ್ರೀಲ್ಯಾನ್ಸರ್ ಲಭ್ಯವಿರುವ ಸೇವಾ ಪ್ರದೇಶಗಳು.

ಪೋರ್ಟ್‌ಫೋಲಿಯೋ

Semantic Search Engine for Clothing Reviews

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

ವಿವರಗಳನ್ನು ವೀಕ್ಷಿಸಿ →

Semantic Search Engine for Clothing Reviews

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

ವಿವರಗಳನ್ನು ವೀಕ್ಷಿಸಿ →

Semantic Search Engine for Clothing Reviews

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

ವಿವರಗಳನ್ನು ವೀಕ್ಷಿಸಿ →

Semantic Search Engine for Clothing Reviews

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

ವಿವರಗಳನ್ನು ವೀಕ್ಷಿಸಿ →

Semantic Search Engine for Clothing Reviews

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

ವಿವರಗಳನ್ನು ವೀಕ್ಷಿಸಿ →

Semantic Search Engine for Clothing Reviews

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

ವಿವರಗಳನ್ನು ವೀಕ್ಷಿಸಿ →

Semantic Search Engine for Clothing Reviews

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

ವಿವರಗಳನ್ನು ವೀಕ್ಷಿಸಿ →
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