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Nagabhushana Raju S

Data Science & AI/ML Engineer

Turning Data & AI into Real-World Innovation with an Entrepreneurial Mindset

Data & AI/ML engineer with an entrepreneurial mindset, skilled at building intelligent, data-driven systems that deliver measurable impact. Experienced applying machine learning to real problems, engineering data pipelines, and shipping AI-powered applications — from multimodal disease detection to real-time computer-vision interfaces.

Experience

Software Development Intern · Apr 2025 – Aug 2025

Getskilled (a unit of GyaanKool Research Labs)

  • Engineered multimodal data pipelines, leveraging ResNet/EfficientNet for medical imaging and BERT/TF-IDF for clinical text.
  • Built and benchmarked fusion models combining image + text features to optimize early-diagnosis precision across modalities.
  • Evaluated models with ROC-AUC, F1-score and confusion matrices; aligned outcomes with clinical relevance alongside data scientists and medical advisors.

Final-Year AI Intern (Bharat Unnati AI Fellowship) · 2025 – 2026 (4 months)

TechLeap with ExpertPedia — VTU

  • Selected for the VTU final-year internship fellowship; delivered applied AI project work with professional mentorship.

Projects

Multimodal AI for Early Disease Detection · 2nd Prize — Project Expo, MIT Mysore

Python · ResNet/EfficientNet · BERT · Fusion Models · Jun 2025 – Present

Real-Time Sign Language Interpreter (Multi-Modal AI)

FastAPI · WebSockets · MediaPipe · Caffe DNN · JS · Jun 2024 – Feb 2025

Early Disease Predictor

Python · Flask · scikit-learn · 2025

Expense Tracker Web App

JavaScript · Charts · localStorage · Mar 2025 – Apr 2025

Skills

PythonJavaCC++R (basics)HTMLCSSJavaScriptMachine LearningEDA / ADAMultimodal AINLPRecommendation SystemsAdvanced Prompt EngineeringPower BITableauExcelData Visualizationn8nGit / GitHubSQLMySQLMongoDBDBMSHadoop / Hive / Pig (basics)Docker (basics)Jenkins (basics)Anaconda / condaKaggle

Branding · https://mr-nagabhushanaraju-s.engineer/

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All exports render on your machine in a second — no server. Designed PDF is the two-column showcase for humans; ATS-friendly PDF is a single-column, plain-text version that Applicant Tracking Systems parse cleanly (use this when applying through job portals). For the LaTeX-compiled version, drop the .tex into Overleaf.