Machine Learning Engineer I 

Over the years, I have worked on projects that strengthened my real-world ML skills and problem-solving ability:

• Developed a GAN-based pneumonia detection model with 94.99% accuracy using chest X-ray data.

• Built a Telecom Complaints Monitoring System using time-series forecasting to improve operational response planning.

• Designed CashShield — an ML-based counterfeit currency detection system + Android app for visually impaired users (95%+ accuracy).

• Performed sales-trend analytics at Osolin Organics to improve revenue strategy and customer insights.

• Published multiple IEEE papers covering machine learning, medical imaging and hardware-efficient computation.

At Purdue, my coursework and hands-on work involve advanced ML models, CNNs, GANs, statistical modeling, and predictive analytics. I frequently work with Python, TensorFlow, NumPy, Pandas, scikit-learn, SQL, Matplotlib, and have earned certifications including Google Data Analytics Professional and Google Cloud Digital Leader.

What drives me is translating ideas into working ML solutions — understanding data deeply, building models that learn meaningfully, and transforming them into outcomes that can make a measurable impact. I value clean experimentation, reproducible workflows, and collaborative building — especially where AI meets real-world adoption.

I am currently open to opportunities in Machine Learning Engineering, Data Science, and Applied AI roles.
Feel free to connect — I’m always excited to discuss ML, projects, innovation, and building intelligent systems that actually ship.