You will get A Web interface AI Agent that answers queries about uploaded documents
Project details
I will build you a custom RAG chat agent that can answer from the data you've uploaded. The chat agent will be hosted in Google Cloud Platforms Compute Engine, and will be backed by several GCP services including Cloud SQL and Vertex AI.
You'll be able to upload files to a Cloud Storage Bucket, and they will be processed into the vector database for generative retrieval using natural language chat in our basic web interface built in Python.
Only text, via PDF, docx, txt, csv, xlsx, and xls is supported out of the box. Image recognition support not included. Revisions do not include new features requiring additional infrastructure resources or endpoints.
You'll be able to upload files to a Cloud Storage Bucket, and they will be processed into the vector database for generative retrieval using natural language chat in our basic web interface built in Python.
Only text, via PDF, docx, txt, csv, xlsx, and xls is supported out of the box. Image recognition support not included. Revisions do not include new features requiring additional infrastructure resources or endpoints.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-3, GPT-4, LaMDAWhat's included
| Service Tiers |
Starter
$600
|
Standard
$1,200
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 2 | 5 | 10 |
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 | - |
About Uzma
Senior ML Engineer | RAG & LLM App Developer (Python, FastAPI)
Karachi, Pakistan - 5:54 pm local time
𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 | 𝟭𝘅 𝗞𝗮𝗴𝗴𝗹𝗲 𝗚𝗿𝗮𝗻𝗱𝗠𝗮𝘀𝘁𝗲𝗿 | 𝟭𝘅 𝗞𝗮𝗴𝗴𝗹𝗲 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸 𝗘𝘅𝗽𝗲𝗿𝘁 | 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 | 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 | 𝗔𝗜 𝗔𝗽𝗽 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 | 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵) | 𝗣𝘆𝘁𝗵𝗼𝗻 | 𝗙𝗮𝘀𝘁𝗔𝗣𝗜
𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗖𝗿𝗲𝗮𝘁𝗼𝗿
I'm a Senior ML Engineer and Kaggle GrandMaster who builds production-grade AI systems from data pipelines to deployed APIs.
I help businesses turn messy data and AI ideas into reliable, working products, across the full AI stack: machine learning, generative AI, computer vision, and data engineering.
𝗟𝗟𝗠, 𝗥𝗔𝗚 & 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: LLM integration, RAG pipelines, AI agent development, multi-agent systems, prompt engineering, fine-tuning, vector databases (Pinecone, ChromaDB, FAISS), semantic search, embeddings, AI chatbots and copilots built with LangChain, LangGraph, OpenAI API, and Hugging Face Transformers.
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: Predictive modeling, classification, regression, time series forecasting, clustering, feature engineering, model evaluation, A/B testing, anomaly detection, and statistical analysis turning raw data into measurable business outcomes like reduced error rates and faster decisions.
𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 & 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: Image classification, object detection, image segmentation, OCR, image generation (Stable Diffusion, GANs), and deep learning model training using PyTorch and TensorFlow.
𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Model deployment, MLOps, REST API development (FastAPI), containerization (Docker), cloud deployment (AWS), CI/CD for ML, scalable backend systems for AI apps, and end-to-end pipeline automation.
𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: ETL pipelines, data cleaning, feature stores, SQL, dashboarding and reporting (Streamlit, Tableau), and exploratory data analysis.
𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: Python, PyTorch, TensorFlow, scikit-learn, Pandas, NumPy, Hugging Face, LangChain, LangGraph, OpenAI API, SQL, FastAPI, Docker, AWS, Streamlit, Tableau, Pinecone, ChromaDB
Kaggle Grand Master verify my rank: 𝗸𝗮𝗴𝗴𝗹𝗲: 𝘂𝘇𝗺𝗮𝗮𝗸𝗵𝘁𝗮𝗿
𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝘄𝗼𝗿𝗸: Github
As a Technical Content Creator, I also document and share my AI/ML work publicly so you can review real code, notebooks, and results before you hire, not just promises.
𝗖𝗼𝗿𝗲 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀: focus, consistency, attention to detail, and reliable delivery.
Let's talk about what you're trying to build.
Steps for completing your project
After purchasing the project, send requirements so Uzma can start the project.
Delivery time starts when Uzma receives requirements from you.
Uzma works on your project following the steps below.
Revisions may occur after the delivery date.
Only step
Client purchases the project and sends requirements.