Machine Learning Software Engineer

Shibani Kumar

The University of Texas at Dallas, Richardson, Texas

+1-619-***-**** *******.*****@********.*** linkedin.com/in/Shibani-Kumar11/ EDUCATION

The University of Texas at Dallas Aug 2024 - Present Master of Science, Information Technology and Management GPA: 3.89 Coursework: Programming in Python, Advanced Statistics, Database Foundations for Business Analytics, Big Data, Predictive Analytics for Data Science, Applied Machine Learning, Applied Deep Learning, BA with R BNM Institute of Technology Jun 2017

Bachelor of Engineering, Telecommunication Engineering TECHNICAL SKILLS

Programming Languages: Python, R, SQL, C++, Algorithms, Data Structures Machine Learning & AI: TensorFlow, PyTorch, Hugging Face Transformers, OpenAI API, NLP, Deep Learning, Model Fine-Tuning Data Analysis & Visualization: Pandas, NumPy, Matplotlib, Seaborn, Power BI, Tableau, Google Analytics Cloud & Deployment: AWS (S3, EC2, Lambda), GCP, Kubernetes, Flask, Streamlit Database & Big Data: MySQL, Cassandra, MongoDB, Hadoop, Spark, AWS DynamoDB Additional Skills: AI Model Optimization, API Development, Data Engineering, MLOps, Version Control (GitHub) PROFESSIONAL EXPERIENCE

Moolya Software Technologies Senior Software Engineer - Hevodata Client Nov 2020 - Jun 2022

• Developed and continuous testing of ETL data pipelines using Python and SQL, enhancing data integrity and reducing ingestion time by 30% while meeting scalable production standards.

• Automated ETL pipelines to integrate data from multiple sources such as Google Analytics,Spotify,FBAds,etc using Selenium reducing manual effort by 25% and ensuring the delivery of accurate, real-time insights to support marketing strategies

• Collaborated with cross-functional teams to create interactive dashboards using Python, Seaborn, and Tableau, facilitating detailed data analysis and performance modeling for onboarding clients worth $3M.

• Analyzed and visualized large datasets with SQL to uncover trends from marketing analytics integrations, increasing query performance by 30%.

• Developed a transformer-based AI model for language translation, enabling seamless text conversion from one language to another, improving communication for international users by 25%.

• Fine-tuned large language models (LLMs) to improve the accuracy and fluency of translations, reducing error rates by 20% compared to baseline models.

Accenture Software Engineer - AT&T Client Dec 2017 - Aug 2020

• Enhanced application reliability by conducting API testing for microservices with Postman and SOAP UI, ensuring robust integration of RESTful and SOAP-based APIs.

• Implemented functional end-to-end testing of the MyAT&T UI application to improve customer journey efficiency and increase overall application stability by 30%.

• Developed and deployed an automated testing framework using Selenium and Python, reducing manual testing efforts by 80% and streamlining test execution workflows.

ACADEMIC PROJECT EXPERIENCE

Job Application Data Analysis Aug 2024 - Dec 2024

• Created a Python class hierarchy using OOP principles to manage job application dataset of over 10,000 records, automating CSV generation from pickle files and reducing manual effort by 40%.

• Performed data cleaning on job application dataset using Pandascreated 7+ detailed visualizations using Matplotlib and Seaborn to analyze key metrics such as interview difficulty, job type distribution, and salary trends, delivering actionable insights for job seekers. HackUTD Project: AI-Powered Text Summarizer with SambaNova API Nov 2024

• Developed an AI-powered text summarization tool using Meta Llama-3.1-8B - Instruct model to generate concise summaries of long- form text.

• Built an interactive UI using Streamlit for seamless user interaction, enabling efficient text input and summary generation.

• Integrated Flask as the backend to handle API requests and process data efficiently with the SambaNova API.

• Demonstrated expertise in AI model fine-tuning, API integration, and cloud-based AI application development. Rainfall Prediction Classifier Dec 2024 - Jan 2025

• Developed a predictive machine learning model to forecast next-day rainfall using the Australian Government's Bureau of Meteorology dataset, leveraging over 140,000 weather observations across multiple features.

• Implemented and evaluated 5+ algorithms- Linear Regression, KNN, Decision Trees, Logistic Regression, and Support Vector Machines (SVM) and achieved a 90% accuracy rate with Logistic Regression after optimizing hyperparameters.

• Applied advanced data preprocessing techniques, including missing value imputation, outlier handling, normalization, and feature engineering, resulting in a significant improvement in model performance and robustness.

• Conducted comprehensive model evaluation using metrics such as Accuracy Score, Jaccard Index, F1-Score, LogLoss, Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2-Score to ensure high-quality. LEADERSHIP AND AWARDS

• Indian Students Association at UT Dallas – General Secretary Jan 2025 - Present

• Information Technology and Management Student Leadership Council – Social Media Officer Jan 2025 - Present

• Apex : Delivery and Profitability award in the individual category issued by Accenture Oct 2108 - Dec 2018

Contact this candidate