Hire Rishvitha J. - Generative AI Engineer

As a Gen AI Engineer at T-Mobile using Telecom Domain data, I led the development and deployment of AI solutions to enhance business processes and decision-making. I built deep learning models for real-time object detection and video analytics using Keras and PyTorch and applied NLP techniques with GPT-4 for sentiment analysis and topic modeling for PDF documents. I integrated LLMs for automated content generation using LangChain and utilized OpenCV for gesture recognition and augmented reality. Additionally, I optimized data pipelines with data engineering teams, implemented cloud-based solutions using Azure DataBricks, and containerized applications with Docker and Kubernetes for scalability. My work focused on improving model performance and ensuring projects adhered to responsible AI practices. Responsibilities: Developed and deployed sophisticated deep learning models for real-time object detection and video analytics using Keras and PyTorch. Improved model accuracy by designing innovative CNN architectures and employing ensemble methods in computer vision tasks. Adapted transformer-based NLP models (NLTK and SpaCy ), including Llama 2 and GPT-3, for sentiment analysis and topic modeling using Hugging face. Integrated large language models (LLMs) into platforms with LangChain, enhancing automated content generation and intelligent search capabilities

As a Sr.AI/ML Engineer using retail domain data, I design and implement scalable data pipelines and robust architectures that power data-driven decision-making across the organization. I leverage tools such as Apache Spark and orchestration frameworks to build efficient ETL workflows, integrating data from diverse sources into our enterprise data warehouse. Collaborating closely with analysts and data scientists, I deliver solutions that streamline analytics and enhance operational insights. My responsibilities include optimizing pipeline performance, enforcing data governance standards, and implementing security protocols to safeguard sensitive data. My contributions support strategic initiatives and drive continuous improvement in our data infrastructure. Responsibilities: Led architectural strategy for key business technology initiatives, focusing on scalable application architecture solutions. Applied domain expertise and application landscape knowledge to define future-state architectures for enterprise-level technology transformations. Participated across the full data science lifecycle including data mining, cleansing, modeling, validation, and visualization while conducting detailed gap analyses. Engineered MapReduce and Spark-based Python modules for predictive analytics and machine learning in a Hadoop environment on AWS; built a Python-streamed distributed random forest.

As a Machine Learning Engineer at Sprint, I designed and deployed scalable data pipelines and predictive models to optimize logistics and enhance business decision-making. I built machine learning solutions using Scikit-learn, Pandas, and Apache Spark on AWS EMR, leveraging real-time data from traffic and weather APIs to forecast truck delays with high accuracy. My responsibilities included automating CI/CD workflows with GitHub Actions and Terraform, containerizing models with Docker, and deploying them via Flask APIs on Amazon ECS. I implemented robust feature engineering and experiment tracking using Hopsworks and MLflow, while also creating interactive dashboards with Streamlit and Tableau for stakeholders. Additionally, I ensured data quality and governance using Informatica IDQ and integrated Collibra to maintain metadata standards across cloud environments. Project 2: Responsibilities: Designed and implemented scalable ETL pipelines using Informatica, Talend, and Apache Spark to support enterprise-wide analytics. Migrated on-premise data systems to cloud platforms like AWS Redshift, GCP BigQuery, and Amazon RDS, improving scalability and speed. Automated batch and real-time ingestion workflows using AWS Glue, Airflow, and PySpark for faster and more reliable data delivery. Developed advanced SQL queries and transformation logic for large datasets sourced from Oracle, Teradata, and flat files.