Software Engineer Data Visualization

Pranati Mareddy

San Francisco, CA R **************@*****.*** Pranati-mareddy Pranati-Mareddy Æ 516-***-**** Skills Summary

• Languages: C, C++, Python, Java, SQL, PL/SQL, HTML, CSS, PHP

• Libraries and Frameworks: TensorFlow, PyTorch, Keras, JSP, Tomcat

• Technologies: AWS, MySQL, DynamoDB, Jenkins, Docker, Kafka, GitHub

• Data Visualization Tools: Matplotlib, Seaborn

Work Experience

• Wipro Hyderabad, TS

– Software Engineer Jul 2020 – Aug 2022

• Enhanced FedEx’s TMS backend by optimizing 5,000+ lines of Java code, using lazy initialization, loop unrolling, and caching, resulting in a 10% increase in processing speed and improved reliability.

• Created dashboards using Grafana and AppDynamics to monitor system performance. Developed custom metrics and alerting mechanisms to enable proactive issue detection and response, resulting in a 25% reduction in system downtime.

• Implemented CI/CD pipelines using Jenkins, reducing deployment times by 50%.

• Developed APIs in Python and PHP, leveraging MySQL and AWS Aurora databases for efficient data management.

• Set up Splunk to collect and analyze logs, creating dashboards and alerts with SPL and data visualization tools, improving operational efficiency and speeding up incident resolution by 20%.

• Created and optimized complex SQL queries and stored procedures to support backend operations, data analysis, and reporting. This improved data processing efficiency by 15%, helping to deliver faster and more accurate business insights.

• IBM Hyderabad, TS

– Applied AI and ML Intern Jan 2020 – May 2020

• Disease Detection in Plant Leaves [Deep Learning]

• Developed a CNN model to diagnose diseases in leaf images, capitalizing on feature extraction.

• Leveraged Python,TensorFlow, and Pandas and trained for 200 epochs.

• Conducted rigorous training and testing spanning 200 epochs on customized dataset, yielding 91% accuracy. Projects and Learnings

• Stock Market Prediction

• Developed and implemented a stock price prediction model using the K-Nearest Neighbors (KNN) algorithm.

• Achieved a 73% accuracy in predicting stock price movements, outperforming traditional methods such as time series analysis and autoregressive models.

• Leveraged Python,TensorFlow and Pandas.

• Bird Species Recognition

• Spearheaded implementation of bird species recognition Machine Learning application.

• Integrated pre-trained models ResNet50 and VGG16 to attain accuracy rates of 98.73% and 87.47%, respectively.

• Utilized Python, Flask, EC2, HTML, CSS, and JavaScript technologies.

• Generative AI

• Developed text summarization solution using Gemini API and prompt engineering techniques, reducing article length by 30% while maintaining content integrity.

• Implemented text classification system leveraging Gemini API for embeddings, achieving 85% accuracy with an Artificial Neural Network model trained on the embedded dataset.

• Implemented Embedded Search with Vertex AI to generate document embeddings, enabling semantic search for relevant content retrieval and facilitating RAG in LLM context prompts using Vector Search. Leadership Experience

• Enhancing Student Performance Prediction with Random Forest Classification

• Researched student performance prediction using advanced classification models (Decision Tree, Random Forest, Na ıve Bayes), achieving accuracy increase over baseline methods.

• Reference: https://www.irjet.net/archives/V7/i8/IRJET-V7I868.pdf Education

California State University, Eastbay Aug 2022 - May 2024 Masters in Computer Science ; GPA: 3.5 / 4

– Relevant courses: Algorithms, Machine Learning, Cyber Security, System Design, Advanced Computer Networks, Advanced Computer Architecture, Web Technologies, and Data Structures

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