Shreyansh Chopra

Developed a machine learning pipeline in Python, employing boosting techniques to enhance model accuracy.
Addressed class imbalance using advanced sampling techniques, including SMOTE and Tomek Links, increasing model precision from 76% to 85% and reducing engine test failures by 15%.
Containerized the pipeline using Docker and leveraged AWS Lambda to process real-time data from PostgreSQL triggers, enabling consistent, scalable deployments.
Designed and deployed an AI-powered multilingual sales enablement platform using AutoGen agent and Azure AI, integrating NLP to automate personalized pitch generation tailored to regional languages.
Utilized a Neo4j knowledge graph database to structure and retrieve interconnected data on regions, retailers, incentive schemes, and historical performance for intelligent pitch customization.
Achieved a 40% reduction in pitch preparation time, accelerating sales team responsiveness.
Engineered a real-time defect detection system for mechanical dies using YOLOv8, a cutting-edge object detection model in computer vision, achieving 90% accuracy for precise defect localization in manufacturing.
Built and deployed an end-to-end data pipeline by annotating 5,000 die images using LabelImg, applying OpenCV for image preprocessing, and storing data in a structured format for efficient model fine-tuning.
Reduced sheet metal part rejections by 20–30%, minimizing material wastage and improving efficiency.

Engine Testing Classification
• Developed a machine learning pipeline in Python, employing boosting techniques to enhance model accuracy.
• Addressed class imbalance using advanced sampling techniques, including SMOTE and Tomek Links,
increasing model precision from 76% to 85% and reducing engine test failures by 15%
• Containerized the pipeline using Docker and leveraged AWS Lambda to process real-time data from
PostgreSQL triggers, enabling consistent, scalable deployments.
Bazaar Sales Optimization
• Designed and deployed an AI-powered multilingual sales enablement platform using AutoGen agent and
Azure AI, integrating NLP to automate personalized pitch generation tailored to regional languages.
• Utilized a Neo4j knowledge graph database to structure and retrieve interconnected data on regions, retailers,
incentive schemes, and historical performance for intelligent pitch customization.
• Achieved a 40% reduction in pitch preparation time, accelerating sales team responsiveness.
Defect Detection in Automotive Manufacturing
• Engineered a real-time defect detection system for mechanical dies using YOLOv8, a cutting-edge object
detection model in computer vision, achieving 90% accuracy for precise defect localization in manufacturing.
• Built and deployed an end-to-end data pipeline by annotating 5,000 die images using LabelImg, applying
OpenCV for image preprocessing, and storing data in a structured format for efficient model fine-tuning.
• Reduced sheet metal part rejections by 20–30%, minimizing material wastage and improving efficiency

Developed a robust Speech-to-Text (STT) conversion module using Whisper AI for high-accuracy transcription, integrating advanced Natural Language Processing (NLP) techniques to uncover customer interaction insights.
Applied sentiment analysis and emotion detection algorithms alongside frequency analysis to classify customer sentiments and key customer grievances and provided strategic recommendations.

Analysis of Customer Care Calls
• Developed a robust Speech-to-Text (STT) conversion module using Whisper AI for high-accuracy transcription,
integrating advanced Natural Language Processing (NLP) techniques to uncover customer interaction insights.
• Applied sentiment analysis and emotion detection algorithms alongside frequency analysis to classify
customer sentiments and key customer grievances and provided strategic recommendations.