class RudrakshJani:
location = "Ahmedabad, Gujarat, India 🇮🇳"
education = "B.E. Computer Engineering — SAL Institute (2024)"
focus = ["Computer Vision", "Deep Learning", "MLOps", "Predictive Modeling"]
currently = "Building production-grade AI systems & exploring cutting-edge CV research"
stack = {
"languages" : ["Python", "SQL", "HTML/CSS"],
"dl_frameworks": ["TensorFlow", "PyTorch", "Keras"],
"ml_tools" : ["Scikit-Learn", "NumPy", "Pandas", "Transformers"],
"mlops" : ["MLflow", "DVC", "Dagshub", "Docker", "Airflow"],
"cloud" : ["AWS", "GCP", "Azure"],
"cv_tools" : ["OpenCV", "CNN", "Autoencoder", "YOLO"],
}
fun_fact = "I believe every pixel of data tells a story — I just help machines read it 📖"|
🔵 Vodafone Intelligent Solutions
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🟣 Karma Technolab
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🟠 Afame Technologies
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⚡ Independent AI/ML Contributor
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End-to-end deep learning pipeline for medical image classification using CNNs. Integrated MLflow for experiment tracking, DVC for data versioning, containerized with Docker, and served via a RESTful Flask API. |
Video analysis system to detect & track players, referees, and ball movements using OpenCV. Applied K-Means team classification and perspective transformation to compute real-world speed and distance metrics. |
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Classification model to predict SpaceX Falcon 9 first-stage reusability using data scraped from SpaceX REST APIs & Wikipedia. Features interactive dashboards via Plotly Dash and geospatial visualizations with Folium. |
Deep learning model to restore high-fidelity images using a custom autoencoder with advanced CNN layers and bespoke loss functions. Rigorously evaluated for production-level reliability and scalability. |
| 🏅 Certification | 🏫 Issuer |
|---|---|
| Machine Learning Specialization | DeepLearning.AI × Stanford University |
| TensorFlow Developer Specialization | DeepLearning.AI |
| TensorFlow: Advanced Techniques Specialization | DeepLearning.AI |
| IBM Data Science Professional | IBM |
| British Airways Data Science Job Simulation | Forage |
