Mohammed Faisal Parvez

AI Engineer | Deep Learning & Computer Vision Enthusiast

Crafting intelligent solutions and pushing the boundaries of AI to solve real-world challenges.

View My Work

About Me

Hello! I'm Faisal, an AI Engineer.

I am passionate about leveraging artificial intelligence and machine learning to build innovative and impactful solutions. My journey in AI is driven by a curiosity for deep learning, computer vision, and the potential of large language models. I thrive on tackling complex problems and continuously expanding my knowledge in this ever-evolving field.

With a strong foundation in model optimization, multimodal AI, and practical development, I aim to contribute to cutting-edge research and create technologies that make a difference.

Technical Skills

Languages
Python TypeScript SQL HTML CSS
Frameworks & Libraries
Django FastAPI LangChain Transformers TensorFlow PyTorch
AI & ML
Deep Learning Computer Vision Machine Learning LLMs RAG ASR Edge AI Multimodal AI Prompt Engineering
Databases & Tools
ChromaDB pdfplumber Eleven Labs Burp Suite Wireshark Hugging Face ONNX Docker
Languages Spoken
English (Fluent) Hindi (Fluent) Urdu (Fluent) Arabic (Fluent)

Mohammed Faisal Parvez

AI Engineer

Experience

AI Engineer Intern

Cybertronix

Nov 2024 – Present

Currently engaged in developing and optimizing AI models, contributing to innovative projects in machine learning and data analysis.

Network Security Intern

Huntmetrics

Aug 2023 – Sep 2023

Gained hands-on experience in network threat analysis, vulnerability assessment, and security protocol implementation.

Projects & Research

Optimizing DL Models Research

Optimizing DL Models: CNN Quantization for FSL

Spearheaded research on Deep Learning model optimization via CNN quantization, enhancing Few-Shot Learning (FSL) capabilities while maintaining 98% accuracy. Publication forthcoming.

Deep Learning CNN Quantization FSL
Paper (Soon)
Ensemble Model Project

Ensemble Model for CIFAR-10 Classification

Developed an ensemble model combining ResNet-50, VGG16, and Xception on CIFAR-10 using stacking, achieving 97.3% accuracy. (First Prize - College Expo)

Ensemble Learning ResNet-50 VGG16 Xception
Code
Smart Street Lighting Project

Smart Street Lighting System

Engineered an innovative smart street lighting system using LDR and transistor technology, reducing energy consumption by 35%. (First Prize - Science Exhibition)

IoT LDR Sensor Energy Efficiency
Code

Awards & Recognitions

First Prize Winner - College Expo

For developing an ensemble model (ResNet-50, VGG16, Xception) on CIFAR-10, achieving 97.3% accuracy.

First Prize Winner - Science Exhibition

For engineering an innovative smart street lighting system reducing energy consumption by 35%.

Education

B.E. in AI & Machine Learning

Lords Institute of Engineering and Technology, Hyderabad

Expected 2025 | GPA: 81%

Relevant Coursework: Machine Vision, Optimization Techniques in Machine Learning, Augmented and Virtual Reality, Genetic Algorithm and Fuzzy Logic.

Class XII - Science

Iqbalia Junior College, Telangana Board (TSBIE)

2021

Class X

International Indian School Al-Jubail, CBSE

2019

Certifications

Responsible & Safe AI System

NPTEL

Big Data Computing

NPTEL

PyTorch Fundamentals

NVIDIA DLI

Let's Connect

I'm always excited to discuss new projects, research collaborations, or opportunities in AI. Feel free to reach out!