Hello I'm
Keerthi Earra
Software Engineer
Hello I'm
Software Engineer
Bachelors of Technology
Major: Information Technology
Masters of Science
Major: Computer and Information Sciences
I am a highly proactive and detail-oriented Software Engineer with expertise in Python, JavaScript, and data analytics and visualization tools such as PowerBI, Excel, and JMP. My background includes optimizing systems, boosting performance, and leading projects in machine learning, React, JavaScript, and PowerBI. I hold a Master’s degree from the University of Florida, with a focus on advanced Data Structures and Computer Networks. Additionally, I am AWS Certified and deeply passionate about cloud technologies and modern development frameworks. Beyond my technical work, I find creative balance in traveling, cooking, and drawing. I am currently seeking full-time roles in Software Engineering or Data Analysis starting in May 2024. Feel free to reach out if you think I’d be a good fit for your team or know of opportunities that match my skills.
Aug 2023 - Present
Utilized Python Libraries, Excel and JMP to analyze and visualize plant phosphorus bioavailability in soils, improving fertilizer recommendations, and designed a responsive website using TerminalFour, HTML, CSS, and REACT to showcase findings.
May 2023 - Aug 2023
Enhanced the Retail Foreign Exchange Platform's reliability and system efficiency by developing new features in Python and implementing strategic dashboards to improve operational insights.
Mar 2022 - Jul 2022
QA Engineer on the Documentum D2 project, focusing on improving CMS with access control and automated workflows, while managing code quality, automating tests, and deploying scalable RESTful APIs to improve system integration.
Apr 2021 - Jun 2021
Acquired proficiency as a trainee in technological stacks and development tools, enhancing knowledge in full-stack development, Agile methodologies, version control systems like Git, and Continuous Integration Continuous Deployment practices.
Implemented GAN and VAE on the MNIST dataset to generate realistic digits, compared their performance using Python and TensorFlow, enhancing understanding of unsupervised learning and generative models.
Led a team of four to design a lightweight CNN for real-time facial expression detection using Global Average Pooling instead of a fully connected layer, and applied Multitask Cascaded CNN for face detection and emotion classification.
Analyzed data from the Global Terrorism report (1974-2018), created visual representations of terrorist incidents illustrating the impact of various factors, and used Linear Regression to predict future casualties.
Participated in the largest gathering of women technologists.
Enhancing leadership and communication skills.
Dedicated to Empowering Underprivileged Communities through Social Welfare Initiatives.
Books that inspire and educate.
Acknowledged for academic excellence.
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