About me

I am Amruth Pai, a passionate and driven graduate student pursuing Business Analytics and Artificial Intelligence at the University of Texas at Dallas. With a strong background in computer science and expertise in machine learning and deep learning, I am committed to harnessing AI and data-driven strategies to solve complex problems and drive innovation.

Current Focus:

  • Deepening expertise in cloud platforms, with a particular emphasis on AWS for scalable and efficient AI and analytics solutions.
  • Progressing through MLOps-focused courses, emphasizing the deployment, monitoring, and optimization of machine learning models in production.
  • Exploring tools and techniques for efficient data preprocessing, pipeline automation, and integration with AI systems.
  • Experimenting with cutting-edge techniques in generative AI, including fine-tuning models for real-world applications.
  • Engaging in team-based initiatives to apply AI and machine learning principles to solve industry-relevant problems.
  • Developing agentic AI solutions that leverage generative models for autonomous task execution, dynamic tool use, and adaptive reasoning.

Projects


  • Satellite Image Segmentation

    Built a Convolutional Neural Network model using U-Net architecture for segmenting satellite images using tensorflow and keras. Deployed the model on hugging face for real time prediction.

    Python, OpenCV, Hugging Face, TensorFlow, Keras

    Learn More

  • LLama-Law(legal advisor chatbot)

    Project was developed as a part of Llama-impact Hackathon This project solves the problem of using government websites easier for common people, navigating and providing legal advice

    Python, Streamlit, GroqCloud, OpenAI API

    Learn More

  • Bitcoin Price Forecasting

    A forecasting pipeline that retrieves real-time Bitcoin price data via API, preprocesses it, and trains an LSTM model to predict future price trends. The workflow is automated through a custom ETL pipeline, orchestrated with Apache Airflow.

    Python, Deep Learning, CICD, Airflow, Dockers, PostgreSQL

    Learn More

  • TumorFlow

    This project implements an end-to-end machine learning pipeline for identifying kidney tumors from CT scan images. The project includes data ingestion, model training, evaluation, and deployment workflows, all managed with MLOps principles.

    Python, Keras, CICD, AWS EC2, Dockers, DVC

    Learn More

  • SQL Agent vs RAG chatbot

    An application to compare the preformance and accuracy of SQL Agent and RAG for tabular data. SQL Agent was suitable for tabular data because of its precise answers and generating queries on the fly.

    Python, Langchain, Streamlit, chromaDB

    Learn More

  • Surge Sense

    A machine learning pipeline to predict surge pricing for cab rides, specifically comparing Uber and Lyft services. The pipeline aimed to leverage historical ride, weather, and event data to forecast price surges. Keeping the focus on MLOps principles and scalability

    Python, Keras, CICD, AWS EC2, Dockers, MLFlow, DVC

    Learn More

  • Face Recognition using CNN

    Built a Convolutional neural network for detecting faces using tensorflow and keras. Augmentation on Images using Albumentations

    Python, Tensorflow, Keras, OpenCV, CUDA

    Learn More

  • Airlines Management System

    Web-based application designed to streamline and automate the management of an airline's core operations. The system integrates features for managing flights, passengers, bookings, employee details, routes, fares, and transactions.

    SQLite, Streamlit, MongoDB, Python

    Learn More

Work Experience

Data Analytics and Software Developer Intern

Van Brunt & Associates, Texas

May 2025 - August 2025
  • Designed and deployed a modular machine learning pipeline to forecast daily and weekly peak loads in the ERCOT grid, achieving an 18% improvement in predictive accuracy.
  • Engineered robust feature sets by integrating ERCOT system forecasts, multi-source weather data, and tariff-based signals such as 4CP demand indicators
  • Built a dual-objective model to simultaneously forecast load and identify 4CP events, attaining 87% precision and reducing commercial billing risk.
  • Incorporated price signals, battery dispatch profiles, and weather anomalies to enhance temporal load sensitivity, resulting in a 14% boost in model performance.
  • Implemented a scalable and reproducible pipeline using Python, SQL, and MLflow, enabling seamless experimentation and production deployment.
Senior Software Engineer

Infosys, Bangalore

August 2019 - March 2024
  • Increased operational efficiency by 15% through diagnosing & resolving client system issues, while developing customized application programs to meet unique client requirements
  • Collaborated with cross-functional teams to ensure consistent system integration and timely project delivery
  • Optimized and refactored code, resulting in 10% improvement in system performance
  • Automated client reports by utilizing DB2 SQL, significantly enhancing reporting accuracy and reducing manual efforts
  • Designed and maintained application programs for IBM I systems using RPGLE and CLLE, ensuring high reliability
Software Engineer Intern

Infosys, Mysore

Jan 2019 - May 2019
  • Led the development of a comprehensive full stack application tailored for Nike retailers, ensuring seamless integration between front-end and back-end components
  • Engineered backend using JAVA, Spring Boot and RESTful Web services API
  • Designed and implemented the front-end interface with Angular and utilized MySQL for database management
Software Engineer Intern

Biztime IT Solutions

May 2018 - July 2018
  • Contributed to the development of ZonalDesk, a mobile application designed to deliver fast and reliable everyday services to people in rural areas with limited accessibility
  • Played a key role in implementing Google Maps API to create an intuitive and functional map interface, enhancing the app’s usability and service accessibility
  • Utilized Android Studio for app development, ensuring a smooth user experience on low-resource devices .

Education

MS Business Analytics and AI

University of Texas at Dallas

GPA: 3.89

August 2024 - May 2026(Expected)

  • Database Foundations for Business Analytics
  • Advanced Statistics for Data Science
  • Business Analytics With R
  • Database Design
  • Data Visualization
  • Applied Machine Learning
  • Big Data
  • Applied Deep Learning
  • Introduction to Robot Manipulation and Navigation
  • Applied Natural Language Processing

BE Computer Science

Nitte Meenakshi Institute of Technology

GPA: 3.70

August 2015 - May 2019

Skills and Interests


  • Python

  • ML

  • NLP

  • Data Engineering

  • Computer Vision

  • Machine Learning & AI

    Machine Learning Algorithms
    PyTorch
    Hugging Face
    Scikit-learn
    Keras
    TensorFlow
    Deep Learning
    Computer Vision
    Natural Language Processing (NLP)
    Large Language Models (LLMs)
    Object Detection and Tracking

    Domain-Specific Knowledge

    Data Science Methodology
    Data-Driven Insights
    Software Engineering

    Generative AI

    RAG
    AI-Agents
    LLM fine-tuning
    Azure OpenAI
    LlamaIndex

  • Big Data & Data Engineering

    Apache Spark
    HDFS
    Spark SQL

    Data Analysis & Visualization

    Pandas
    Matplotlib
    Plotly
    Seaborn
    Tableau

    Programming Languages

    Python (proficient)
    JAVA (proficient)
    C++
    RPGLE
    CLLE
    R

    Databases

    MongoDB
    MySQL
    chroma DB
    FASSIA

    DevOps & Cloud

    Firebase
    Linux
    AWS
    CICD
    GitHub Actions
    Dockers
    Apache Airflow