P Tejaswini

Computer Science undergraduate · Aspiring Software Engineer

Bangalore, Karnataka, India

@p_tejaswini

0 followers

🎓 B.E. in Computer Science and Engineering at AMC Engineering College · Graduating 2027

About

Motivated Computer Science undergraduate with hands-on experience in full-stack development, artificial intelligence, and machine learning. Interested in building innovative software solutions while continuing to grow in technical problem-solving and model development.

Experience

Machine Learning Intern

Corizo

Mar 2026 – May 2026

Developed and evaluated machine learning models for real-world problem-solving; performed data preprocessing, feature engineering, exploratory data analysis, model validation, and compared multiple ML approaches.

Education

2027

B.E. in Computer Science and Engineering

AMC Engineering College

Computer Science · Aug 2023 – Aug 2027

Computer Science · Aug 2023 – Aug 2027
12th Grade (PUC)

Carmel Convent PU College

Skills

3 to 6 months

NumPy Python Pandas scikit-learn Data Analysis Machine Learning

<3 months

C Oop Java LLMs XGBoost LangChain AI Agents JavaScript Advanced DBMS Data Annotation Prompt Engineering Natural Language Processing Data Structures and Algorithms

Tools / apps / platforms

<3 months

Git MySQL GitHub MongoDB Google Sheets Microsoft Excel

Projects

AI Resume Screening Agent

Python, LangChain, LLMs, NLP, Prompt Engineering, PDF Processing

Built an AI-powered agent to automate resume screening and candidate evaluation; extracted candidate skills, qualifications, experience, and keywords from resumes and generated relevance scores for ranking.
AI Hallucination Detector Agent

Python, LangChain, LLMs, NLP, Prompt Engineering, APIs

Developed an AI agent to detect hallucinations and unsupported claims in LLM-generated responses; implemented fact-checking, response validation, and confidence-based verification mechanisms.
Live Network Intrusion Detection System (NIDS)

Python, Scapy, Scikit-learn, Pandas, NumPy, Machine Learning

Developed a real-time NIDS to monitor and analyze live network traffic for potential security threats; captured live packets using Scapy and applied machine-learning techniques to classify traffic and detect intrusions or anomalies.

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