I will develop custom ai machine learning and deep learning projects
Full Stack AI Engineer
About this Gig
Are you looking for a custom, high-quality deep learning solution tailored to your data?
You're in the right place!
As a skilled AI engineer with 3 years of experience, I will help you build, train, and deploy deep learning models using TensorFlow, PyTorch, and Keras. Whether you're working on image classification, NLP, object detection, anomaly detection, or recommendation systems, I've got you covered.
What I Offer:
- Model development (CNN, RNN, LSTM, Transformers, etc.)
- Data preprocessing and feature engineering
- Model training, tuning, and evaluation
- Visualizations (loss/accuracy curves, confusion matrix)
- Streamlit or Flask-based deployment (Premium)
- API integration
- Documentation (ppt,pdf,word,latex)
I work with real-world datasets and deliver production-grade, scalable solutions.
Message me before placing an order to discuss your dataset and requirements in detail.
Let's bring your AI ideas to life!
Programming language:
Python
•
R
•
MATLAB
•
Colab
APIs:
Microsoft Computer Vision AI
•
Azure Face API
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
•
TensorFlow
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What do I need to get started?
Please provide a clear problem statement, dataset (or data source), and any specific requirements like the type of model or task (e.g., classification, object detection).
Can you work with custom datasets?
Yes! I can work with your own dataset, whether it’s in CSV, image folders, audio files, or other formats. I’ll help with preprocessing too.
Which frameworks do you use?
I primarily use TensorFlow, Keras, and PyTorch, depending on the project requirements.
Will you help me understand the model/code?
Absolutely. I provide clean, well-commented code and can include a brief explanation or walkthrough on request.
Can you deploy the model as an API or web app?
Yes, in the Premium package I offer deployment using Streamlit, Flask, or FastAPI along with documentation.

