Achyut Jagini

Advanced Micro Devices (AMD) — Machine Learning Intern

Jan 2023 – Jul 2023

• Built a Flask-based internal web application integrating a Siamese Neural Network to detect duplicate JIRA defect tickets.
• Integrated Pinecone as a vector database to store and retrieve embedding vectors for efficient similarity search within the Flask application.
• Designed REST APIs and reduced manual defect triage time by ~50%.
• Applied software engineering best practices including CI/CD, version control, and API testing.
• Collaborated with cross-functional engineering teams to improve defect management workflows.

Master’s Thesis — Improving Customer Churn Prediction

Jan 2025 – Jun 2025

• Developed ML models (Logistic Regression, Random Forest, CatBoost) to improve churn recall.
• Applied explainability techniques (SHAP, LIME) for business interpretability.
• Containerized the system using Docker and deployed on AWS EC2.

IEEE Publication — Bitcoin Price Prediction

Twitter Sentiment Analysis for Bitcoin Price Prediction
View on IEEE Xplore

• Built NLP pipelines for real-time sentiment extraction.
• Integrated sentiment features with time-series models.

MERN Stack Dashboard Application

• React, Node.js, Express, MongoDB, Redux
• JWT-based authentication, CRUD, pagination
• Material-UI and responsive design

Kafka-Based Crime Data Analysis Platform

• Real-time data streaming with Kafka, Spark, MongoDB
• Cluster monitoring using CMAK and Zookeeper
• Fault-tolerant producers and consumers

Machine Learning for Crime Classification

• Spark Streaming pipeline for real-time inference
• Logistic Regression and Random Forest models

Linux AppArmor Security Profiles

• Custom MAC profiles for sandboxing applications
• Prevented unauthorized privilege escalation