You will get I will build a custom RAG system to chat with your documents using AI
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Top Rated
Project details
I specialize in building production-ready RAG (Retrieval-Augmented Generation) systems that allow users to interact with their own data using AI. Unlike basic chatbot wrappers, I design complete pipelines that include document processing, embeddings, vector search, and LLM integration.
With strong experience in Python and backend development, I build systems using FastAPI, ensuring clean architecture, scalability, and easy integration into existing applications. My approach focuses on writing modular, maintainable code so your system can grow as your needs evolve.
Whether you need a simple chatbot API or a more advanced RAG system, I aim to deliver reliable, well-structured solutions that are ready for real-world use. I also provide clear documentation and guidance so you can confidently run and extend your system after delivery.
With strong experience in Python and backend development, I build systems using FastAPI, ensuring clean architecture, scalability, and easy integration into existing applications. My approach focuses on writing modular, maintainable code so your system can grow as your needs evolve.
Whether you need a simple chatbot API or a more advanced RAG system, I aim to deliver reliable, well-structured solutions that are ready for real-world use. I also provide clear documentation and guidance so you can confidently run and extend your system after delivery.
Database Type
MySQL, MS SQL, SQLite, PostgreSQLWhat's included
| Service Tiers |
Starter
$100
|
Standard
$300
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 10 days |
Number of Revisions | 1 | 2 | 2 |
Source Code |
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NM
Nahid M.
May 4, 2024
Algorithm learning (Leet code)
He is very helpful. I recommend him for learning algorithms.
SP
Suraj P.
Aug 12, 2023
Data structure and algorithms questions (Need to hire multiple people)
Sileshi was very helpful. He was able to complete the project on time. Highly recommended.
About Sileshi
Python Backend Engineer | AI/RAG | AWS | Full Stack
100%
Job Success
Addis Ababa, Ethiopia - 6:59 pm local time
Recently, I built and deployed a full-stack RAG application using FastAPI, Next.js, PostgreSQL (pgvector), Redis, Docker, AWS EC2, and CI/CD workflows. The system included document ingestion pipelines, semantic/vector search, async APIs, caching, and frontend integration.
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✅ WHAT I CAN HELP WITH
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✅ AI / RAG Applications
✅ FastAPI / Flask / Django Backend Development
✅ REST APIs & Automation Systems
✅ PostgreSQL & SQL
✅ Docker & AWS Deployment
✅ CI/CD Workflows
✅ Full Stack Web Applications
✅ React / Next.js Frontend Development
✅ Async Python Applications
✅ Data Processing & Backend Debugging
✅ Existing Codebase Maintenance & Refactoring
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🛠️ TECH STACK
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🔹 Python, SQL, Go, JavaScript, C#
🔹 FastAPI, Flask, Django
🔹 React, Next.js
🔹 PostgreSQL, Redis
🔹 Docker, GitHub Actions, CI/CD
🔹 AWS EC2
🔹 Vector Search & Embedding Pipelines
🔹 Git / Linux / APIs
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🚀 RECENT EXPERIENCE
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🟢 Built and deployed a full-stack RAG platform with vector search, document ingestion, async APIs, caching, and AWS deployment
🟢 Worked on AI training/evaluation projects involving real-world GitHub issues, debugging model-generated code, and preparing reproducible Docker environments
🟢 Prepared Python and SQL technical interview content for DataLemur, including coding questions, hints, and detailed solutions
🟢 Solved 1000+ DSA problems with strong focus on algorithms, debugging, edge cases, and performance optimization
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💡 HOW I WORK
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✔️ Clean & maintainable code
✔️ Strong debugging & problem-solving skills
✔️ Fast learner who can quickly understand existing systems
✔️ Comfortable working independently
✔️ Focused on practical, production-ready solutions
If you need help building, improving, or debugging backend/AI systems, feel free to reach out.
#AI
#RAG
#Fullstack
#Python
#React
#SQL
#GO
Steps for completing your project
After purchasing the project, send requirements so Sileshi can start the project.
Delivery time starts when Sileshi receives requirements from you.
Sileshi works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Planning
Discuss your use case, data sources, and define the system requirements
System Design
Design the RAG pipeline including embeddings, retrieval, and API structure