You will get Scalable FastAPI Backend with AI Integrations
Project details
I design enterprise-grade AI backend systems that are secure, scalable, and optimized for real-world production. From LLM integration to cloud deployment and MLOps, I deliver clean architecture that supports growth.
AI Algorithms
Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, Automatic Speech Recognition, Conversational AI, Image Processing, Image Recognition, Natural Language Understanding, Sentiment Analysis, Speech Synthesis, Text RecognitionAI Tools
Gradio, Hugging Face, Microsoft 365 Copilot, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, LLaMA, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 15 days |
Number of Revisions | 2 | 3 | 4 |
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.
Additional Revision
+$25About Eisha
AI & Backend Engineer | FastAPI | Python | LLM | GCP
Faisalabad, Pakistan - 5:03 pm local time
In my current role, I architect and maintain Python/FastAPI backends for multiple production platforms, integrating AI-powered features including automated evaluation, feedback generation, and voice-based interaction, alongside AI automation pipelines for data processing, classification, and content workflows. I manage end-to-end data pipelines processing 10,000+ documents monthly, from raw ingestion through LLM-assisted labeling, OCR extraction, and structured output to live app integration.
My AI toolkit includes RAG systems (FAISS, SentenceTransformers), LangGraph-based agent workflows, fine-tuning with LoRA (Qwen-1.5-4B), and daily integration of Gemini and OpenAI APIs. I deploy on GCP Cloud Run with Docker and Kubernetes, and have built event-driven pipelines using GCP Pub/Sub.
Available for LLM integration, API integration, AI feature development, backend architecture, and data engineering work.
Steps for completing your project
After purchasing the project, send requirements so Eisha can start the project.
Delivery time starts when Eisha receives requirements from you.
Eisha works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis
Review project scope, AI use case, tech stack, and deployment goals. Clarify expectations and finalize architecture plan.
System Architecture & Design
Design backend structure, database schema, API contracts, and AI integration flow.