I have hands-on experience in designing structured ML pipelines that integrate data versioning, experiment tracking, model management, and cloud-based training and deployment. I enjoy transforming research ideas into practical, deployable solutions using clean code architecture and industry-standard tools.
My technical interests include machine learning, deep learning, MLOps, cloud-based ML systems, and decision-focused optimization. I continuously work on improving both my technical skills and communication abilities, aiming to grow as a professional AI/ML engineer capable of contributing to real-world, impactful projects.
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Machine Learning & Deep Learning
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MLOps & Model Lifecycle Management
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Experiment Tracking & Reproducibility
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Cloud ML (AWS SageMaker)
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Data Versioning & Collaborative ML
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End-to-End ML System Design
To build robust, scalable, and production-grade AI systems while continuously learning and applying modern machine learning and MLOps practices in real-world applications.
Languages & Frameworks
Python · TensorFlow · PyTorch · Scikit-Learn · FastAPI · Flask · Streamlit · Solidity
ML / DL
XGBoost · Naïve Bayes · YOLO · Transformers · BERT · LLaMA · K-Means · Random Forest
MLOps & Tools
Docker · Kubernetes · MLflow · DVC · Airflow · GitHub Actions · AWS · GCP · Azure
