AI Engineer | Machine Learning Enthusiast | Open Source Contributor
I am an innovative and results-driven AI Engineer with over 4 years of experience in Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing (NLP). Throughout my career, I have directed over 25 impactful projects, boosting process efficiency by 40% and generating $200,000 in revenue. My passion lies in developing cutting-edge AI solutions, leveraging state-of-the-art technologies to solve complex problems, and driving technological advancements.
I have a proven track record in building scalable AI models, optimizing system performance, and deploying them across cloud and edge platforms. I've worked on diverse AI applications ranging from Generative AI, Multi-Agent Systems, LLMs, to Real-Time Object Detection and PPE Recognition, and have led teams to successfully deliver AI-driven products that exceed client expectations.
- 🏆 Gold Medalist in Bachelor of Artificial Intelligence from Air University
- 🌟 Top Rated Freelancer on Upwork with 50+ projects completed
- 🧠 Continuous Learner and AI Enthusiast
Machine Learning: TensorFlow, PyTorch, Keras, Scikit-Learn, Time Series Forecasting
Deep Learning: CNNs, RNNs, LSTMs, GANs, Transformers
Computer Vision: OpenCV, YOLO, Semantic Segmentation, Pose Estimation, Real-Time Object Detection
Natural Language Processing: spaCy, NLTK, Transformers, Sentiment Analysis, Text Classification, Information Retrieval
API Development: Flask, FastAPI, Django REST Framework
Cloud Deployment: AWS (EC2, Sagemaker, Lambda), Azure (Cognitive Services, Machine Learning)
Edge Deployment: NVIDIA Jetson Nano, Xavier
Languages: Python (Proficient), JavaScript, C++, SQL
Development Tools: Docker, Git, GitHub, CI/CD
A cutting-edge AI model built using NLP and deep learning to automatically detect fake news and misinformation online by analyzing content credibility and its sources. This project utilizes Transformer models for enhanced accuracy and scalability.
Developed a generative adversarial network (GAN) for creating original artwork based on input descriptions. This project uses advanced style transfer and image synthesis to produce high-quality and realistic art pieces.
A highly interactive and intelligent AI assistant designed to manage daily tasks, provide information, and enhance productivity using natural language understanding and personalized AI agents.
A touchless employee attendance system that uses computer vision and machine learning to increase accuracy and efficiency in attendance tracking, reducing human error and improving workflow efficiency.
A predictive machine learning model that achieves 88% accuracy in detecting cardiac arrhythmia from ECG data, aiding in early diagnosis and improving patient outcomes through automated medical assistance.
Real-time computer vision models deployed to detect Personal Protective Equipment (PPE) in industrial environments, reducing safety incidents by 40% by ensuring compliance with safety protocols.
Architected customized generative AI models (RAG Systems) that improved information retrieval and task-specific performance for enterprise clients, leading to a 42% increase in data accuracy and client satisfaction.
- Enhancing Human Activity Recognition through Integrated Multimodal Analysis
Published a paper detailing the integration of multiple modalities for improving human activity recognition.
- Email: meermoazzam41@gmail.com
- LinkedIn: Moazzam Ali
- LeetCode: Moazzam Ali
- HackerRank: Moazzam Ali
- ORCID: Moazzam Ali
