You will get Enterprise AI Document Processing Backend with LLM Integration
Rising Talent
Project details
I will build an AI document-processing backend with LLM integration, scalable APIs, and cloud deployment support — from a focused prototype to a production-ready platform.
The solution can include document ingestion, extraction and classification, asynchronous processing, structured data output, and reliable backend workflows using Python/Django, RabbitMQ, PostgreSQL, Kubernetes, and LLM services such as vLLM.
The solution can include document ingestion, extraction and classification, asynchronous processing, structured data output, and reliable backend workflows using Python/Django, RabbitMQ, PostgreSQL, Kubernetes, and LLM services such as vLLM.
Machine Learning Tools
Azure Machine Learning, ChatGPT, NVIDIA AI Platform, Python, PyTorchWhat's included
| Service Tiers |
Starter
$800
|
Standard
$2,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100
Architecture & Operations Runbook
(+ 2 Days)
+$300
Additional Environment Deployment
(+ 5 Days)
+$500
Additional Data Source Integration
(+ 3 Days)
+$300Frequently asked questions
About Nayem
Backend & AI Infra Engineer | Python, Kubernetes, LLM Inference
Dhaka, Bangladesh - 8:26 am local time
Day to day, I work with Python/Django services, PostgreSQL, RabbitMQ pipelines, Kubernetes workloads, LLM inference infrastructure, and complex performance and reliability problems in live production systems.
How I Help Clients
✔ Build and optimize scalable Python/Django/FastAPI backend systems
✔ Design secure REST APIs and asynchronous processing pipelines using RabbitMQ
✔ Deploy and operate production workloads on Kubernetes, AKS, and OpenShift
✔ Deploy and optimize production LLM inference using vLLM
✔ Build AI document-processing workflows for extraction, classification, and LLM-powered processing
✔ Build CI/CD and GitOps workflows with GitHub Actions and Argo CD
✔ Troubleshoot backend, Kubernetes, database, networking, and performance issues
Core Technologies
Python, Django, FastAPI, PostgreSQL, RabbitMQ, Redis, Docker, Kubernetes, AKS, OpenShift, Microsoft Azure, GitHub Actions, Argo CD, and vLLM.
If you need help with a Python backend, Kubernetes platform, production LLM deployment, or AI document-processing system, send me a short description of your setup and the problem you're trying to solve.
Steps for completing your project
After purchasing the project, send requirements so Nayem can start the project.
Delivery time starts when Nayem receives requirements from you.
Nayem works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Architecture Planning
Review your requirements, existing systems, data flow, and define the backend architecture and implementation approach.
Backend Development & AI Integration
Develop backend services, APIs, database models, and integrate AI/LLM capabilities according to your workflow.