AI/ML Engineer

Aspiring Data Scientist | ML/AI Engineer | Full-Stack ML Developer

San Francisco, US.

About

Highly motivated and results-oriented professional with a strong foundation in Python, machine learning, and deep learning frameworks (PyTorch, TensorFlow). Eager to leverage expertise in computer vision, model deployment (FastAPI, Flutter), and UI/UX design (Figma, HTML/CSS) to develop innovative AI-driven solutions. Seeking a challenging Data Science, ML, or AI Engineering role to contribute to cutting-edge projects and drive impactful technological advancements.

Education

University Name

University City, State, United States of America
Bachelor of Science / Master of Science Computer Science / Data Science / Artificial Intelligence Grade: 3.8/4.0

Courses

Machine Learning

Deep Learning

Computer Vision

Data Structures & Algorithms

Statistical Methods for Data Science

Languages

English

Skills

Programming Languages

Python, HTML, CSS.

Machine Learning & Deep Learning

PyTorch, TensorFlow, Computer Vision, OpenCV, Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Model Training & Evaluation.

MLOps & Deployment

FastAPI, RESTful APIs, Docker, Git, Flutter (Mobile ML Deployment).

Data Science Tools

NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn.

Design & Prototyping

Figma, Adobe After Effects, UI/UX Design Principles.

Projects

Real-time Object Detection with FastAPI & OpenCV

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Summary

Developed an end-to-end real-time object detection system capable of identifying and localizing multiple objects within live video streams. The project integrated advanced Computer Vision techniques with a scalable API for deployment.

Mobile-first Image Classification with Flutter & TensorFlow Lite

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Summary

Created a mobile application for image classification, demonstrating the deployment of machine learning models on edge devices. This project focused on optimizing model performance for mobile environments.

AI-Powered Content Recommendation System

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Summary

Built a content recommendation engine using collaborative filtering and deep learning techniques to personalize user experiences. The system aimed to increase user engagement and content discovery.