Node Js Machine Learning

Neha Nishal Goud S

623-***-**** # ******@*****.*** ï linkedin.com/in/neha-nishal-goud-s/ § github.com/nehanishal001 EDUCATION

Arizona State University Tempe, Arizona

Master of Science in Software Engineering, GPA: 3.9/4.0 Aug 2023 - May 2025 TECHNICAL SKILLS

Programming Languages: Python, Java, SQL, JavaScript, TypeScript, C++ AI Machine Learning: NLP (LUIS, RAG), LangChain, TensorFlow, Hugging Face Databases: Snowflake, Oracle, PostgreSQL, MongoDB, Redis Web API Technologies: Node.js, React, Spring Boot, Flask, Django, REST APIs, OpenAPI Cloud DevOps: AWS, Microsoft Azure, Docker, Kubernetes, Jenkins, Git, CI/CD Pipelines Security: Fortify, WebInspect, Sonatype, OAuth 2.0, JWT WORK EXPERIENCE

Full Stack Developer, Darwinbox Hyderabad, India

AI-Driven Chatbot Integration Apr. 2022 – Jul. 2023

• Integrated Microsoft Bot Framework with Node.js, TypeScript, and Azure Bot Services to deploy an AI-powered chatbot in Microsoft Teams, improving HR query resolution efficiency by 40%.

• Developed AI-driven automation using LUIS NLP and RESTful APIs (Node.js, Express), streamlining HR workflows and reducing manual interventions.

• Built adaptive cards with JSON schema to enable real-time notifications and approval workflows in Teams, reducing approval time by 50%.

• Implemented role-based access control (RBAC) using JWT authentication and OAuth 2.0, enhancing security for chatbot interactions across organizations.

• Developed a configurable chatbot admin panel using React and Node.js, enabling enterprises to dynamically control bot availability and enforce organization-wide enable/disable settings.

• Optimized AI intent closure by integrating context retention in Azure Bot Services, reducing redundant steps and improving workflow efficiency by 40%.

• Created a testing framework using Mocha, Jest, and Chai, achieving 90% test coverage and automating tests within the CI/CD pipeline for seamless deployments. Software Engineer, Fiserv Pune, India

Exception Processing and Transaction Research Aug. 2021 – Mar. 2022

• Developed and optimized RESTful APIs using Java, Spring Boot, and OpenAPI for the STAR application, reducing exception processing times by 50%.

• Designed SQL scripts in Snowflake and Oracle, improving transaction research query performance by 30% and ensuring 90% accuracy in financial data.

• Implemented microservices architecture and Kafka-based messaging, improving real-time exception handling.

• Automated CI/CD pipelines with Jenkins and Azure DevOps, reducing release cycle time by 95%.

• Resolved security vulnerabilities using Fortify, WebInspect, and Sonatype, in compliance with PCI DSS standards. Software Engineer Intern, Kore.ai Hyderabad, India XO Platform - Bot Builder Jan 2021 – Jul 2021

• Integrated New Relic to monitor features, including RabbitMQ queues, WebSocket calls, Celery tasks, and a C++ application, reducing debugging time from 2 hours to 1 minute and significantly enhancing system efficiency.

• Implemented the RAG (Retrieval-Augmented Generation) approach using LangChain and LlamaIndex, optimizing database search and retrieval processes to improve answer generation accuracy for enterprise datasets.

• Enhanced the SearchAI App’s data integration capabilities by integrating Confluence, SharePoint, Zendesk, and ServiceNow through API development and optimized MongoDB queries, expanding available data by 90%.

• Managed deployments and oversaw NLP tasks, including efficacy testing of Hugging Face models for specific use cases, which improved query accuracy by 40% and enhanced documentation comprehensiveness. PROJECTS

Health Insurance Charge Prediction Model

• Developed a full-stack machine learning application using Python (Flask, NumPy, Pandas, Scikit-learn) for regression-based insurance charge prediction with 78% accuracy.

• Implemented data preprocessing, feature selection, and hyperparameter tuning using NumPy, Pandas, and Scikit-learn, optimizing model performance.

• Deployed the trained model on IBM Cloud with an automated API (Node-RED), enabling real-time predictions through a responsive UI.

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