Browse categories
Explore
Fiverr Pro
English
$
USD
I will architect your ai retrieval system
Saad
About this gig
I am a RAG Engineer specializing in designing and deploying production-ready Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge bases. My systems guarantee high-fidelity answers by connecting LLMs (GPT-4, Llama, etc.) directly to your data.
I transform unreliable prototypes into scalable, mission-critical applications.
What I Deliver:
End-to-End RAG Development: Full pipeline build, from data ingestion to deployment.
Vector Database Expertise: Implementation and optimization using FAISS, Milvus, and ChromaDB.
Advanced Retrieval: Engineer Hybrid Search (Sparse + Dense) and Dynamic Context Injection for maximum relevance and recall.
Scalability & Performance: Design intelligent Chunking, Embedding Update, and Caching Strategies to handle millions of documents efficiently.
Guaranteed Quality: Develop Evaluation Metrics & Dashboards to measure and ensure high relevance, recall, and system stability.
I offer proven, enterprise-grade RAG solutions to unlock your datas potential.
Ready for a reliable AI retrieval system?
Contact me to discuss your project!
Get to know Saad
Saad
Data Science and Blockchain Engineer with L2 Experience
- FromPakistan
- Member sinceJul 2023
- Avg. response time2 days
Languages
English
With over 8 years of hands on experience in data-driven systems and distributed technologies, I help businesses turn complex data and blockchain infrastructure into reliable, production-ready solutions.
My background spans data analysis, large-scale data pipelines, and blockchain system design, with a strong focus on building solutions that are accurate, scalable, and easy to maintain. I’ve worked on projects ranging from analytical platforms and backend data services to decentralized systems and Layer 2 blockchain architectures, ensuring performance, security, and long-term stability.
My Portfolio
FAQ
Which tech do you use?
I Will Be Using : FAISS, Milvus, ChromaDB and any major LLM (GPT-4, Claude, Llama)
RAG vs. Fine-Tuning?
RAG is better for factual, real-time answers and is more cost-efficient. Fine-Tuning changes the model's style.
What info do you need?
Your knowledge base access, data volume, and the specific use case you want to build (e.g., Q&A chatbot).
