Hi, I’m Keerthi Shivapur, a computer science student with a passion for AI and ML. I’m currently pursuing a BE in Computer Science and Engineering at Bapuji Institute of Engineering and Technology, with hands-on experience in building embedding-based retrieval systems and Retrieval-Augmented Generation (RAG) applications. I enjoy experimenting with prompt engineering, vector search, and evaluating LLM outputs for accuracy and relevance. Looking to apply structured testing, annotation, and evaluation to improve AI systems, I am excited about roles in AI/ML development, data science, and software engineering. I thrive on learning new techniques and collaborating on practical projects that push the boundaries of AI.…

Keerthi Shivapur

Hi, I’m Keerthi Shivapur, a computer science student with a passion for AI and ML. I’m currently pursuing a BE in Computer Science and Engineering at Bapuji Institute of Engineering and Technology, with hands-on experience in building embedding-based retrieval systems and Retrieval-Augmented Generation (RAG) applications. I enjoy experimenting with prompt engineering, vector search, and evaluating LLM outputs for accuracy and relevance. Looking to apply structured testing, annotation, and evaluation to improve AI systems, I am excited about roles in AI/ML development, data science, and software engineering. I thrive on learning new techniques and collaborating on practical projects that push the boundaries of AI.…

Available to hire

Hi, I’m Keerthi Shivapur, a computer science student with a passion for AI and ML. I’m currently pursuing a BE in Computer Science and Engineering at Bapuji Institute of Engineering and Technology, with hands-on experience in building embedding-based retrieval systems and Retrieval-Augmented Generation (RAG) applications. I enjoy experimenting with prompt engineering, vector search, and evaluating LLM outputs for accuracy and relevance.

Looking to apply structured testing, annotation, and evaluation to improve AI systems, I am excited about roles in AI/ML development, data science, and software engineering. I thrive on learning new techniques and collaborating on practical projects that push the boundaries of AI.

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Language

English
Advanced
Kannada
Fluent
Telugu
Intermediate
Hindi
Intermediate

Work Experience

AIML Intern at SuperMentr Technology
February 9, 2026 - May 30, 2026

Education

Bachelor of Engineering (B.E.) - Computer Science and Engineering at Bapuji Institute of Engineering and Technology
March 20, 2020 - April 20, 2022
Pre-University Certificate (PUC) - Sri Vidyaniketan PU College at Davanagere PU College
March 20, 2020 - April 20, 2022
Bachelor of Engineering (B.E.) in Computer Science and Engineering at Bapuji Institute of Engineering and Technology
March 20, 2022 - May 20, 2026

Qualifications

Bachhelor of Engineering
December 5, 2022 - May 30, 2026

Industry Experience

Software & Internet, Media & Entertainment, Education, Professional Services, Other
    paper Enterprise AI Knowledge Assistant
    • Developed an Enterprise AI Knowledge Assistant using FastAPI to enable intelligent querying of organizational documents through natural language.
    • Implemented a Retrieval-Augmented Generation (RAG) pipeline integrating document search with large language models to generate context-aware responses.
    • Built secure authentication and user management APIs including registration, email verification, password reset, and role-based access control.
    • Designed APIs for query history, analytics, system health monitoring, and feedback tracking for improved AI performance and observability.
    • Implemented document upload and semantic search capabilities for efficient knowledge retrieval across enterprise data sources.
    • Developed scalable backend architecture supporting streaming responses and multi-user organization management.
    paper Cross-Modal Audio Retrieval System (Text → Audio Search)

    Tech: Python, YAMNet (TensorFlow Hub), FAISS,NumPy
    •Built a semantic audio retrieval system that retrieves relevant audio samples using natural language queries
    •Preprocessed audio (MP3 → WAV, 16 kHz mono) and generated 1024-dimensional audio embeddings using
    YAMNet
    •Indexed embeddings using FAISS and performed similarity search with cosine similarity
    •Trained a lightweight classifier and evaluated performance using confusion matrix
    •Enabled metadata-free semantic audio search using text queries without relying on multimodal like CLAP