You will get a production RAG pipeline with retrieval tuning and evaluation
Project details
I build production RAG pipelines, not prototypes.
What sets this apart from most RAG freelancers: I focus on retrieval quality, not just wiring an LLM to a vector store. That means proper chunking strategy, hybrid search, re-ranking, and actual evaluation, so you know the system works before you ship it. Everything I claim is live, published, or on GitHub, so you can verify it before hiring me.
What sets this apart from most RAG freelancers: I focus on retrieval quality, not just wiring an LLM to a vector store. That means proper chunking strategy, hybrid search, re-ranking, and actual evaluation, so you know the system works before you ship it. Everything I claim is live, published, or on GitHub, so you can verify it before hiring me.
AI Algorithms
Convolutional Neural Network, Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Text-to-Speech, AI-Generated Code, Conversational AI, Text RecognitionAI Development Language
PythonAI Tools
Hugging Face, PyTorch, TensorFlow, Word2vecAI Models
BERT, ChatGPT, GPT-3, GPT-4, GPT-NeoWhat's included
| Service Tiers |
Starter
$70
|
Standard
$180
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 1 | 3 | 5 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | |
Model Documentation | - | - | |
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | - | ||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$15 - $35
Additional Revision
+$10Frequently asked questions
About Manas
Go + Python AI Engineer | High-Performance LLM & RAG Backends
Chandigarh, India - 10:26 am local time
This isn't a theory. I built Keiro, a self-hostable adaptive RAG infrastructure, exactly this way: a Go API gateway handling routing, rate limiting, and an in-process semantic cache, talking over gRPC to a Python intelligence layer running three-tier adaptive retrieval. It's live at trykeiro.in and published as a pip package.
Benchmarked on 180 domain questions across 3 independent LLM judges:
• +5.4pp context recall on complex queries
• +6.1pp on multi-hop reasoning
• 85% routing accuracy on the hardest query tier
I also published SwiftPredict, a pip-installable AutoML library (PyPI), and led a team building a production conversational AI backend (RAG + JWT auth + voice STT/TTS + Redis) recognized at Google's GenAI Hackathon 2025.
Where I can help:
• Go services for high-throughput backend systems (APIs, gateways, workers)
• Python/PyTorch for the ML and LLM layer
• Go and Python systems via gRPC when you need both performance and ML capability in one pipeline
• RAG pipelines: retrieval tuning, hybrid search, re-ranking, evaluation
• FastAPI backends, async task queues, caching, rate limiting
I'm early on Upwork but not early in the work. Everything above is live, published, or on GitHub, so you can verify it before hiring me.
I scope clearly before starting and would rather under-promise on timeline than miss one.
Steps for completing your project
After purchasing the project, send requirements so Manas can start the project.
Delivery time starts when Manas receives requirements from you.
Manas works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & data review
I review your data sources and use cases to design the right retrieval strategy and chunking approach for your content.
Pipeline setup & ingestion
I set up the vector database, ingest and chunk your data, and configure the embedding model.