H
Himabindu Voruganti
About
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Missouri, United States
Résumé
Jobs
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- CAI/ML EngineerCisco, TX, USAJan 2024 - Current (2 years 7 months)• Engineered predictive ML algorithms using Python and XGBoost, improving network traffic forecasting accuracy by 28%, enabling proactive bandwidth allocation and optimizing enterprise network efficiency. • Developed deep learning pipelines with PyTorch and TensorFlow, reducing false positive alerts by 22% and strengthening real-time security monitoring across large scale enterprise systems. • Automated model training, validation, and deployment pipelines using Docker and CI/CD frameworks, reducing deployment timelines by 40% and accelerating enterprise AI solution delivery. • Executed end-to-end data preprocessing and feature engineering across multi-source datasets, achieving 99% data integrity and enhancing predictive model reliability a
- MAI/ML EngineerMphasis, IndiaJan 2021 - Dec 2022 (2 years)• Designed regression and classification ML models, improving supply chain forecasting accuracy by 35%, reducing stockouts, operational delays, and enhancing overall logistics efficiency for clients. • Applied clustering and segmentation techniques for customer behavior analytics, increasing marketing campaign effectiveness by 18% and boosting client engagement and ROI. • Built end-to-end ML pipelines with Python, Scikit-learn, and Flask, enabling real-time insights and accelerating data driven decision-making for enterprise applications. • Performed advanced data wrangling, cleaning, and feature engineering on structured and unstructured datasets, reducing missing data errors by 25% and improving model reliability. • Monitored post-deploym
- NNAJun 2020 - Dec 2020 (7 months)NA
Education
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- AMasters in Information SystemsAuburn University, AL, USAMay 2024
- SBachelors in Information TechnologySri Indu College of Engineering and Technology, IndiaJul 2021
Projects (professional or personal)
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- Customer Support Chatbot Using Large Language ModelsJul 2021• Fine-tuned a pre-trained Hugging Face Transformer on enterprise support data to improve contextual understanding and response accuracy. • Improved an interactive Flask-based interface for real-time customer query handling and dynamic response generation. • Deployed the chatbot using Docker and AWS SageMaker, enabling scalable, low-latency performance for high-volume interactions. • Integrated analytics to monitor user interactions, satisfaction, and model drift, supporting continuous improvement of chatbot performance.