# Venkatesh Nagubandi > M.S. Computer Science and B.S. Computer Science from the University of California, Santa Cruz Location: Tracy, California, United States Profile: https://flows.cv/venkateshnagubandi ## Work Experience ### Software Engineer @ Genea Jan 2024 – Present ● AI Chatbot for User Access Analytics - Designed and developed an intelligent SQL chatbot leveraging Python, LangChain, and LangGraph, powered by ChatGPT-4.0, to create AI flows that allow users to interact with a database of user access data. - Engineered and deployed the chatbot application in a Dockerized environment, ensuring robust scalability, portability, and production-level reliability. - Implemented a Retrieval-Augmented Generation (RAG) approach with ChromaDB to enhance response accuracy and relevance by efficiently retrieving relevant examples - Utilized Python and Boto3 with AWS Athena for scalable database queries and S3 for storing results and outputs. - Developed a REST API using Python Flask to enable seamless communication between the chatbot backend and a Node.js-based frontend, ensuring efficient data exchange and a smooth user experience. ### Machine Learning Engineer @ Applied Materials Jan 2023 – Jan 2024 | Santa Clara County, California, United States ● Spares Forecasting Machine Learning Project - Authored SQL queries in Hadoop to extract, transform data from multiple different data sources to create a unified dataset. - Performed data cleaning and exploratory data analysis using Pandas and Matplotlib, identifying errors, anomalies, and trends in the dataset. - Applied mean imputation to handle missing values, target encoding for categorical variables, and information gain for feature selection, resulting in a more refined dataset for modeling - Experimented with multiple models and identified LightGBM as the best performer through cross-validation reducing forecast error (MAPE) by 80% compared to baseline models. - Deployed the LightGBM model in production, enabling risk assessment and sales forecasting, with results visualized through Tableau and Power BI dashboards to support business decisions. ### Machine Learning Researcher: Capstone Project @ University of California, Santa Cruz Jan 2023 – Jan 2024 | Santa Cruz, California, United States - Conducted an in-depth analysis of Multimodal Large Language Models (MLLMs) in Visual Question Answering (VQA) tasks across both synthetic and real-world datasets. - Compared four distinct MLLM methods: ChatGPT4v, Gemini Pro Vision, LLAVA 1.6, and PICA, using the OKVQA, GQA, VQA v2, and AGVQA datasets. - Sorted VQA questions into perception and cognition tasks, rigorously testing model skills in object recognition and complex reasoning. - Sorted VQA questions into perception and cognition tasks, rigorously testing model skills in object recognition and complex reasoning. - Outlined strengths and limitations of current MLLM methodologies, providing actionable insights to enhance model training and scalability in production environments. ### Teaching Assistant @ University of California, Santa Cruz Jan 2022 – Jan 2024 - Facilitated student understanding of key Probability and Statistics concepts, including Bayes' Theorem and statistical inference, by demonstrating practical applications in experimental design and the law of large number - Instructed on essential math concepts for machine learning—probability, linear algebra, and optimization—through practical examples and algorithm demonstrations to enhance model evaluation techniques. - Guided students in mastering Python programming fundamentals, covering data types, control flow, methods, and Object-Oriented Programming (OOP) to build a strong foundation for advanced ML implementations ### Machine Learning Researcher: Capstone Project @ University of California, Santa Cruz Jan 2022 – Jan 2023 | Santa Cruz, California, USA - Led the development of a specialized Visual Question Answering (VQA) dataset tailored for the agricultural domain, enhancing the accuracy of machine learning models in answering domain-specific queries - Deployed advanced web scraping techniques using BS4 and Python to extract relevant forum data from Agtalk, enriching the dataset’s comprehensiveness and relevance. - Applied Natural Language Processing (NLP) methods, including regex and fine-tuning a BERT model from Huggingface, to accurately discern and categorize pertinent questions- ### Software Engineer @ Laurus Technologies LLC Jan 2022 – Jan 2023 | Tracy, California, United States ● Customer Support AI Agent - Developed and deployed a production-ready AI-powered customer support agent using LangChain and RAG (Retrieval-Augmented Generation) over internal documents and FAQs with ChromaDB. - Built a FastAPI backend with modular REST endpoints for Q&A, user feedback logging, and session management, enabling seamless integration into existing web and chat platforms. - Designed and implemented a CI/CD pipeline using GitHub Actions for automated testing, linting, Docker image builds, and versioned deployments to AWS, ensuring fast and reliable code delivery. - Configured automated monitoring and logging via AWS CloudWatch, enabling real-time error tracking and performance metrics for proactive debugging and uptime monitoring. ● Edtech Prediction Project - Deployed an end-to-end AI pipeline for course price prediction in Edtech using Fast API and Streamlit with Python, which improved pricing strategy efficiency by enabling real-time predictions - Conducted Exploratory Data Analysis (EDA) on various data attributes using Pandas with Python, which provided insights into key statistics for better decision-making - Implemented a robust MySQL database solution to optimize data storage, retrieval, and management processes, reducing query times and improving data integrity. - Applied Hyperparameter Tuning on various PyCaret models, selecting Random Forest Regressor for its high performance, achieving a final accuracy of 94.5% ### Machine Learning Researcher: Capstone Project @ UCSC Mental Health Predatory App Research Jan 2021 – Jan 2021 | Santa Cruz, California, United States ● Developed machine learning models (Naive Bayes, Decision Trees, MLP, SVMs) with Sklearn to detect fraud in app reviews, thereby improving detection accuracy and reducing false positives. ● Utilized feature selection techniques to optimize model performance within linguistic data, enhancing processing efficiency and overall accuracy. ● Implemented a PyTorch-based recurrent neural network (RNN) for classification, using pre-trained GloVe word embeddings as the input feature layer. ● Implemented regularization methods such as L1 and L2 to prevent overfitting and improve model generalization, achieving a final accuracy of 93.3%, precision of 83.3%, and recall of 82.4%. ## Education ### Masters in Computer Science University of California, Santa Cruz ### Bachelor's degree in Computer Science University of California, Santa Cruz ## Contact & Social - LinkedIn: https://linkedin.com/in/v-nagubandi --- Source: https://flows.cv/venkateshnagubandi JSON Resume: https://flows.cv/venkateshnagubandi/resume.json Last updated: 2026-04-05