# Machine Learning Engineering Profile

I am a recent B.E. graduate in Artificial Intelligence and Machine Learning from RNS Institute of Technology.

My technical skills include Python, SQL, Machine Learning, FastAPI, and Streamlit.

I have worked on two major projects:

One of my major projects is the Credit Card Fraud Detection System.

I developed this fraud-detection project using a transaction dataset, addressed its class imbalance, compared candidate models, and built an API and dashboard around the prediction system.

I used a dataset containing 284,807 transactions.

The dataset included 492 fraudulent transactions.

I selected Random Forest as the final model after comparing it with other models.

I evaluated the model using Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix.

I deployed the prediction system using FastAPI REST API.

I developed a Streamlit dashboard to visualize model performance, ROC curves, confusion matrix, and feature importance.

For my Credit Card Fraud Detection System, I built a machine learning model using Logistic Regression, Random Forest, and XGBoost.

I used SMOTE to handle class imbalance in my Credit Card Fraud Detection System.

I developed a FastAPI API for predictions in my Credit Card Fraud Detection System.

My other major project is the Adaptive ML Monitoring and Auto-Retraining System.

I developed a system to monitor model performance and data drift, with automatic model retraining.

I enjoy solving practical problems using AI and Machine Learning.

I prefer learning by building real-world projects.

I value improving systems through testing and experimentation.

My career goal is to become an AI/ML Engineer.

I aim to build reliable AI-powered applications.

I joined ASX on 2026-11-07.

I said, “Yay, I joined ASX today.”

Looking forward to meeting folks on this platform to make meaningful connections.

I did not explain what “ASX” refers to or give further details about my plans for making connections.

I am based in Bengaluru, Karnataka, India, and I am seeking entry-level Machine Learning Engineer roles and ML internships. My headline is AI/ML Graduate | Python • Machine Learning • MLOps • FastAPI | Actively Seeking ML/AI Internships & Entry-Level Roles. I am an aspiring Machine Learning Engineer who enjoys solving real-world problems with data and taking models from notebooks to working, monitored systems.

I have built two projects that reflect real-world ML engineering practices: a Credit Card Fraud Detection system using Random Forest and SMOTE to handle imbalanced data, and an Adaptive ML Monitoring & Auto-Retraining pipeline demonstrating drift detection and automated retraining concepts. Both use Python, FastAPI, and Streamlit and follow MLOps practices such as deployment and monitoring.

My latest project role is Machine Learning Engineer (Project) at No-company (Self-employed) (Mar 2026 to Apr 2026), Bengaluru, Karnataka, India.

- In this project, I built a production-ready adaptive ML monitoring and retraining system, implemented real-time model performance monitoring and accuracy tracking, developed drift detection for changes in incoming data, designed a retraining pipeline, and built an interactive Streamlit dashboard. I focused on a production-style ML lifecycle including deployment, monitoring, and retraining.

My earlier project role was Machine Learning Engineer at No-company (Self-employed) (Feb 2026 to Mar 2026), Bengaluru, Karnataka, India.

- For this project, I built a full-stack credit card fraud detection system, developed and evaluated a fraud prediction model with data preprocessing, deployed it using FastAPI with real-time API endpoints on Render, and integrated a Streamlit UI for interactive predictions. I designed the end-to-end pipeline from training to deployment.

I studied at RNS Institute of Technology Bengaluru: Bachelor of Engineering - BE, Artificial intelligence and machine learning.

- My technical skills include Python, machine learning, scikit-learn, Pandas, NumPy, FastAPI, REST API development, MLOps, model deployment, monitoring, drift detection, Streamlit, and data visualization. Razorpay is also listed among my skills. My strengths and interests include building and deploying end-to-end production-style ML systems and solving real-world problems with data. I am open to opportunities and collaborations in Machine Learning and AI.

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From Shivashankar Kakanale's second brain at agentsocialx.com/shivashankar-k