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Kalaga Vaishnavi

AI/ML Intern • Visakhapatnam, India • k****************@gmail.com • +91*******701 • drivetube.ai/•••••

Professional Summary

AI/ML Intern with 1+ years of experience building machine learning models and Python applications; skilled in TensorFlow, supervised learning, data preprocessing, and software development workflows. Seeking an internship or entry-level role to contribute to model development, data pipelines, and production-ready ML solutions while continuing to learn and expand practical experience.

Technical Skills

Programming Languages: Python,Java
Web Technologies: HTML,CSS
Frameworks and Libraries: TensorFlow,Keras,scikit-learn,NLTK,spaCy,Pandas,NumPy
Databases: SQL
Data and Analytics: Data preprocessing,Data augmentation,Model evaluation
Tools and Methodologies: Jupyter Notebook,Google Colab,VS Code,Git,GitHub
Core Concepts: Object-oriented programming,Machine learning supervised learning,Natural language processing,Data structures & algorithms,Operating systems,Computer networks

Work Experience

Vault of Codes
Python Development Intern
Dec 2025 – Dec 2025
Worked on hands-on Python software development tasks building utility applications and demonstrating encryption, persistence, and OOP principles.
Tech Stack: Python, OOP, File handling, Git, VS Code
  • Designed and implemented a Secret Code Generator using Caesar cipher in Python to demonstrate encryption/decryption logic and modular code structure.
  • Built an Expense Tracker with file-based persistent storage, implementing CRUD operations and input validation using Python file handling and OOP.
  • Developed a command-line Personal To-Do List application applying OOP design patterns to manage tasks, priorities and persistence across sessions.
  • Structured code into reusable modules and classes to separate business logic from I/O, improving maintainability and reusability across utilities.
  • Documented functionality and usage with README-style instructions and sample usage examples to simplify handoff and review.
  • Used Git for version control and followed iterative development workflows during the internship, committing changes and managing code history.
IBM SkillsBuild
Artificial Intelligence Intern
Dec 2024 – Dec 2024
Participated in IBM SkillsBuild AI learning modules, building ML solutions including image classification and recommendation systems to apply supervised learning and basic NLP.
Tech Stack: Python, TensorFlow, scikit-learn, Pandas, NLP TF-IDF
  • Trained a multi-class animal image classification model using TensorFlow, implementing data pipelines for image loading, preprocessing and batching.
  • Applied image augmentation and preprocessing techniques to expand training data and improve model robustness and generalization.
  • Evaluated model performance using standard metrics and confusion analysis to iterate on model architecture and preprocessing steps.
  • Built a movie recommendation system using collaborative filtering techniques to model user-item interactions and generate ranked suggestions.
  • Enhanced recommendations with basic NLP feature extraction (TF-IDF) on movie metadata to incorporate content signals alongside collaborative signals.
  • Logged experiments and documented model configurations, training procedures and evaluation results to support reproducibility.
Google for Developers
AI & ML Virtual Intern
Oct 2024 – Oct 2024
Completed Google for Developers virtual internship modules focused on supervised learning, data preprocessing and end-to-end ML workflows using real-world datasets.
Tech Stack: Jupyter Notebook, Google Colab, Python, TensorFlow, scikit-learn
  • Completed hands-on supervised learning modules covering data collection, labeling and preprocessing workflows using Jupyter and Colab notebooks.
  • Implemented feature engineering and data cleaning techniques to prepare datasets for model training and validated transformations with sample data.
  • Explored model selection and hyperparameter tuning approaches to compare performance across candidate algorithms.
  • Applied cross-validation and evaluation metrics to assess model stability and generalization on held-out datasets.
  • Documented end-to-end experiments and produced reproducible notebooks that demonstrated model training, evaluation and interpretation.
  • Gained practical exposure to the full ML lifecycle including dataset preparation, model training, evaluation and iteration best practices.

Projects

Multi-Class Animal Classification
Tools Used: Python, TensorFlow, Data preprocessing, Data augmentation, Pandas, NumPy
  • Trained an image classification model to distinguish multiple animal categories using labeled image datasets and supervised learning workflows.
  • Applied preprocessing and augmentation (resizing, normalization, flips) to increase dataset variability and improve model generalization.
  • Iterated on model architecture and evaluated using validation splits to refine performance and reduce overfitting.
Movie Recommendation System
Tools Used: Python, Collaborative filtering, NLP, scikit-learn, Pandas
  • Built a recommendation engine employing collaborative filtering to suggest movies based on user-item interactions and preference data.
  • Extracted content features from movie descriptions using basic NLP (TF-IDF) to augment collaborative signals and improve suggestion relevance.
  • Evaluated recommendation relevance with holdout tests and refined feature integration to improve rank ordering of suggestions.

Education

Vignan's Institute of Information Technology
B.Tech in Information Technology • Visakhapatnam, India • Sept 2023 – Expected 2027
Sri Chaitanya Junior College
Intermediate (MPC) • Visakhapatnam, India • Sept 2021 – May 2023
Z. P. High School
SSC (Andhra Pradesh) • Anakapalli, India • June 2019 – May 2021

Certifications

TCS NQT — 66% — TCS National Qualifier Test • 2026
Generative AI Learning Path — Google Cloud Skill Boost
PCAP - Programming Essentials in Python — Cisco
Programming, Data Structures and Algorithms using Python — NPTEL
Java Programming Fundamentals — edX

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