Kovvuri Aditya R.

Key Skills

Software

Other

Label Studio

CVAT

Top Subject Matter

Enterprise document-based search and question answering (RAG)

Custom object detection model training using YOLOv8

building local AI Chatbots

Top Data Types

Document

Video

Image

Top Task Types

Question Answering

Object Detection

Bounding Box

Classification

Polygon

Segmentation

Text Generation

Computer Programming/Coding

Freelancer Overview

Graduate Apprentice Trainee (ML/AI integration) — built offline LLM + RAG for document-based Q&A. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Ollama and Other. Education includes Master of Technology, Kakinada Institute of Technology (2024) and Bachelor of Technology, Vellore Institute of Technology (2023). AI-training focus includes data types such as Document and Video and labeling workflows including Question Answering and Object Detection.

Labeling Experience

Graduate Apprentice Trainee — YOLOv8 custom object detection model training

OtherVideoObject Detection

Trained a YOLOv8 model for custom object detection tasks to enable real-time detection in applied computer vision scenarios. Worked on integrating the detection model into larger flight-dynamics and flight-data workflows. Conducted model training efforts using deep learning tooling for deployment-oriented outcomes. • Trained YOLOv8 for custom object detection • Focused on performance for detection on real-world data inputs • Integrated ML model outputs into downstream pipelines • Collaborated with senior scientists on ML-driven simulation enhancements

2025 - 2026

Graduate Apprentice Trainee (ML/AI integration) — built offline LLM + RAG for document-based Q&A

DocumentQuestion Answering

Built RAG-based document question answering to support contextual search and information retrieval for offline AI use cases. Used local LLM components to generate answers grounded in enterprise document content. Focused on secure multi-user access for confidential document interaction. • Implemented Retrieval-Augmented Generation (RAG) pipelines for contextual search • Prepared document-based workflows for Q&A using local embeddings • Integrated offline LLM ecosystem components for retrieval and generation • Supported multi-user secure access for document QA

2025 - 2026

Education

Work History