ML engineer focused on designing, training, and deploying AI systems — not demos, real products.
I care about interpretability, reliable inference, and building infrastructure that holds up under pressure. Currently deepening expertise in MLOps, cloud engineering, and AI for healthcare & sustainability.
Depth over hype · Clarity over noise · Real execution over surface-level experimentation
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🧠 Brain Tumor Detection Deep learning medical imaging system. Grad-CAM explainability for clinical relevance.
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📉 Customer Churn Platform End-to-end pipeline on real Telco data. EDA → modeling → evaluation → deployment.
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🌱 Environmental Intelligence AI-powered analytics for sustainability. Research-driven problem solving.
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ml_stack = ["PyTorch", "TensorFlow", "scikit-learn", "MLflow", "Pandas", "NumPy"]
viz = ["Matplotlib", "Power BI", "Streamlit"]
languages = ["Python", "JavaScript", "SQL", "R"]
web = ["Next.js", "Node.js", "Express.js", "MongoDB", "MySQL"]
devops = ["Git", "Docker*", "CI/CD*", "AWS*", "GCP*"] # * = actively learningFocused on mastery · Building meaningful AI · Thinking long-term