Akash Giri

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Classification

Question Answering

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I am a detail-oriented Data Annotation and AI Evaluation Specialist with over two years of hands-on experience supporting machine learning projects through high-quality data labeling and QA validation. My background in Computer Science has equipped me to work effectively with diverse datasets, including text, image, conversational, and OCR data, while maintaining annotation accuracy rates of 97–99%. I am skilled in using industry-standard tools such as CVAT, Labelbox, SuperAnnotate, Prodigy, and Label Studio, and have contributed to AI model fine-tuning by delivering consistent, reliable training data. My expertise includes guideline interpretation, error analysis, dataset validation, and peer review, and I have experience documenting workflows and training new annotators to ensure quality and compliance. I am passionate about supporting the development of robust AI systems through meticulous data preparation and evaluation.

Labeling Experience

AI Text Annotation & Response Evaluation Project

AppenTextEntity Ner ClassificationClassification

Worked on AI training and data labeling projects focused on improving the performance of large language models. The project involved annotating and evaluating text-based datasets used for NLP model training. Key responsibilities included: Classifying text data based on intent, topic, and relevance Performing Named Entity Recognition (NER) for people, locations, organizations, and key terms. Evaluating AI-generated responses for accuracy, fluency, and contextual relevance. Comparing multiple model outputs and selecting the best response based on predefined quality metrics. Writing and refining prompt–response pairs for supervised fine-tuning (SFT). Following strict annotation guidelines, quality benchmarks, and confidentiality standards. Handled medium to large-scale datasets while maintaining high accuracy and consistency under tight deadlines.

Worked on AI training and data labeling projects focused on improving the performance of large language models. The project involved annotating and evaluating text-based datasets used for NLP model training. Key responsibilities included: Classifying text data based on intent, topic, and relevance Performing Named Entity Recognition (NER) for people, locations, organizations, and key terms. Evaluating AI-generated responses for accuracy, fluency, and contextual relevance. Comparing multiple model outputs and selecting the best response based on predefined quality metrics. Writing and refining prompt–response pairs for supervised fine-tuning (SFT). Following strict annotation guidelines, quality benchmarks, and confidentiality standards. Handled medium to large-scale datasets while maintaining high accuracy and consistency under tight deadlines.

2023

Education

Work History