Hari V.

Key Skills

Software

Label Studio

CVAT

Roboflow

Top Subject Matter

Healthcare — Medical Imaging & Diagnostic Data

Finance — Risk Analysis & Fraud Detection

E-commerce — Product Categorization & Customer Support

Top Data Types

Image

Video

Text

Top Task Types

Evaluation/Rating

RLHF

Object Detection

Classification

Data Collection

Text Generation

Question Answering

Red Teaming

Bounding Box

Freelancer Overview

Hari Vuguru Email: [email protected] Mobile: +91 9676228346 LinkedIn: linkedin.com/in/harivuguru Summary Enthusiastic Computer Science student passionate about software development and building reliable, scalable applications. Strong foundation in programming, data structures, and problem-solving, with project experience in machine learning and cybersecurity tools. Seeking opportunities to contribute to real-world software solutions. Education Andhra Loyola Institute of Engineering and Technology Vijayawada B.Tech in Computer Science and Engineering; CGPA: 8.2 Expected 2026 Sri Chaitanya Junior College Visakhapatnam Intermediate; Marks: 94.1% 2020 – 2022 Jawahar Navodaya Vidyalaya Vizianagaram 10th; Marks: 83.4% 2020 Skills Programming: Python Python Libraries & Frameworks: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras, Tkinter Frontend: HTML, CSS Databases: Oracle SQL, MySQL SQL Skills: Writing complex SQL queries, CRUD operations, Joins, Subqueries, Aggregate Functions, GROUP BY, HAVING, ORDER BY, WHERE clause, Window Functions (RANK, DENSE RANK, LEAD, LAG), Pseudocolumns (ROWNUM, ROWID), Normalization, Constraints Tools: VS Code, Git, GitHub, MySQL Workbench, IntelliJ IDEA Core Subjects: Data Structures, DBMS Internships Cyber Security Analyst — Supraja Technologies (May 2024 – Aug 2024, Vijayawada): Assisted in analyzing network vulnerabilities and identifying potential security threats. Performed basic penetration testing and monitored system

Labeling Experience

Agricultural Data Preprocessing & Labeling

TextClassification

Preprocessed and labeled agricultural datasets including soil composition and environmental variables for a Multicrop Prediction ML model. Ensured consistent labeling across training, validation, and test splits. Performed feature selection and data cleaning to maintain high annotation quality that directly improved model prediction accuracy.

2025 - 2025

Education

Work History