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Kalepu Ramya Sri Sai

Machine Learning Engineer • k****************@gmail.com • linkedin.com/••••• • ramyalucky.netlify.app/•••••

Professional Summary

Machine Learning Engineer with 0 years of experience applying machine learning and deep learning techniques to medical imaging and applied data problems through internships and academic projects; proficient in Python, TensorFlow/Keras, Scikit-learn, and cloud deployment on AWS.

Technical Skills

Programming Languages: Python,C
Web Technologies: REST APIs
Frameworks and Libraries: NumPy,Pandas,Scikit-learn,TensorFlow,Keras,Flask,FastAPI,Matplotlib,Seaborn
Databases: MySQL,PostgreSQL
Cloud and DevOps: Streamlit,AWS EC2,AWS S3,Linux
Data and Analytics: Machine Learning,Deep Learning,Convolutional Neural Networks,Transfer Learning,Image Classification,Jupyter Notebook
Tools and Methodologies: Git,GitHub
Networking & Security Fundamentals: OSI Model,TCP,IP,DNS,DHCP,Routing & Switching,Subnetting,Firewalls,Network Security
Operating Systems: Windows

Work Experience

AWS Academy
Cloud Foundations Intern
Oct 2024 – Dec 2024
Completed AWS Cloud Foundations training focused on EC2 and S3 to deploy and manage cloud-hosted applications and storage workflows.
Tech Stack: AWS EC2, AWS S3, Git, GitHub, Linux, Windows
  • Deployed prototype web applications to AWS EC2 and hosted static assets on S3 to validate end-to-end availability and storage workflows using sample code and containerless deployments.
  • Configured security groups and basic IAM-level access during lab exercises to enforce least-privilege access for deployed resources and secure application endpoints.
  • Integrated project repositories with Git and GitHub for version-controlled deployments and used Git-based workflows to track changes to cloud deployment scripts.
  • Implemented S3 storage patterns for static content and practiced object lifecycle/versioning concepts to manage dataset iterations and backups.
  • Documented cloud architecture and deployment steps in lab reports, producing repeatable runbooks for application deployment and storage configuration.
  • Performed hands-on troubleshooting of networking and instance issues on EC2, applying fundamentals of networking and Linux commands to restore service availability.
Infosys Springboard
Data Science Intern
Apr 2025 – Jul 2025
Internship in data science focused on data preprocessing, exploratory analysis, visualization, and statistical analysis using Python to support data-driven solutions.
Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Flask, FastAPI, MySQL, PostgreSQL, Jupyter Notebook, Git, GitHub
  • Performed data cleaning and feature engineering on heterogeneous datasets using Pandas and NumPy to prepare inputs for model development and reduce missing-value impact.
  • Conducted exploratory data analysis and created visualizations with Matplotlib and Seaborn to surface key patterns and candidate predictors for supervised models.
  • Built and evaluated supervised learning models with Scikit-learn, applying cross-validation and hyperparameter tuning to improve predictive performance on validation sets.
  • Developed prototype model-serving endpoints using Flask and FastAPI to enable programmatic inference for downstream application integration and testing.
  • Worked with relational databases (MySQL, PostgreSQL) to ingest and store cleaned datasets, designing reproducible ETL steps for analysis workflows.
  • Prepared Jupyter Notebook reports and presentations summarizing insights and recommended next steps to internship mentors and project stakeholders.

Projects

Brain Tumour Classification System
Tools Used: Python, TensorFlow, Keras, EfficientNet-B0, Deep Learning, Streamlit
  • Developed an image classification pipeline using EfficientNet-B0 to detect brain tumour classes, achieving 96% accuracy on the validation set through transfer learning and augmentation.
  • Implemented data preprocessing, augmentation, and model training in TensorFlow/Keras and performed evaluation with confusion matrices and class-wise metrics.
  • Packaged the trained model and built a Streamlit app to provide a simple web UI for uploading scans and viewing model predictions for demonstration purposes.
Glaucoma Detection
Tools Used: Python, TensorFlow, Keras, EfficientNet-B7, Deep Learning, Flask, FastAPI
  • Designed and trained a convolutional model based on EfficientNet-B7 to classify glaucoma from retinal images, reaching 97% accuracy on the validation set using transfer learning and fine-tuning.
  • Conducted preprocessing steps specific to retinal imaging including resizing, normalization, and augmentation to improve generalization across datasets.
  • Deployed model inference endpoints with Flask and FastAPI for demo integration and testing of prediction workflows with sample client requests.

Education

Vignan’s Institute of Information Technology
B.Tech CSE (Artificial Intelligence & Data Science) • 2022 – 2026

Certifications

CCNA Modules 1, 2 & 3 (Networking Fundamentals) — Cisco Networking Academy
Python for Data Science — NPTEL
Deep Learning for Natural Language Processing — NPTEL

Achievements

  • Naukri Young Turks 2025 - 99% score, AIR 2903 — 2025: Secured 99% score and All India Rank 2903 in Naukri Young Turks 2025 competition.

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