Nur I.

Key Skills

Software

Lionbridge

Telus

Top Subject Matter

Indonesian generative AI / LLM evaluation and linguistic annotation

Conversational AI language datasets (NLU/NLG) and linguistic annotation

Top Data Types

Text

Image

Video

Top Task Types

Text Generation

Fine-tuning

Evaluation/Rating

Prompt + Response Writing (SFT)

Freelancer Overview

AI Linguist and Language Data Specialist with 6+ years of experience supporting Google language technologies (Google Assistant) and Generative AI (Gemini) initiatives. Experienced in LLM evaluation, response assessment, fact verification, prompt design, linguistic annotation, NLU, NLG, and AI quality assurance. Skilled in evaluating model outputs for accuracy, relevance, instruction adherence, and linguistic quality, while contributing to annotation guideline development, language data curation, and feature testing. Holds a Master's degree in Forensic Linguistics from Vrije Universiteit Amsterdam and brings a unique combination of linguistic expertise, analytical thinking, and AI evaluation experience.

Labeling Experience

LLM Evaluation Linguist - Google

Text

Evaluated Gemini-related large language model outputs for Indonesian language use cases to ensure linguistic quality and user-centered performance. Conducted response assessment with a focus on instruction adherence, relevance, and overall usability while using fact-checking to improve reliability. Supported prompt creation and iterative testing for model evaluation workflows, leveraging expertise in linguistics and AI quality assurance. • Evaluated AI responses for linguistic quality, instruction adherence, and relevance • Performed fact-checking and content verification to identify inaccuracies • Created and refined prompts for model evaluation, testing, and training workflows • Conducted linguistic review and quality feedback to improve model and dataset performance

2024 - 2026

Associate Linguistic Expert (Gemini LLM training and evaluation; annotation quality review) • Google (via Cognizant through Aceolution)

Text

Contributed to Gemini-related LLM training and evaluation for Indonesian language use cases by assessing AI-generated responses. Conducted fact-checking and content verification to identify inaccuracies and improve model reliability. Performed linguistic annotation and quality review on language datasets to support generative AI development and continuous data-quality improvements. • Evaluated responses for linguistic quality, instruction adherence, relevance, and user experience • Created and refined prompts for model evaluation, testing, and training workflows • Tested new AI features and provided structured feedback on language quality and usability • Reviewed annotation defects and supported annotation guideline development for consistent standards

2024 - 2026

Associate Linguist (Language Quality) - Google

Text

Supported Indonesian locale language development and quality improvement initiatives for Google Assistant, with emphasis on Smart Home experiences. Developed and maintained linguistic resources, grammars, and language data to strengthen natural language understanding and natural language generation. Reviewed and validated high volumes of user utterances while ensuring annotation consistency through peer review and guideline adherence. • Developed and maintained linguistic resources supporting NLU and NLG • Reviewed and validated user utterances to improve annotation quality • Performed linguistic annotation, data analysis, and error identification • Trained linguists and maintained documentation and annotation guidelines

2019 - 2023

Associate Linguist (Google Assistant annotation; peer review; guideline-driven consistency) • Google (via TELUS International through Emerhub)

TextEntity (NER) Classification

Supported Google Assistant language development and quality improvement for the Indonesian locale by performing linguistic annotation and validation of user utterances. Served as a first-layer peer reviewer to conduct quality checks and provide feedback before further review stages. Collaborated with a linguist team to ensure annotation consistency and adherence to project guidelines. • Reviewed and validated ~3,000 user utterances per month to improve annotation quality • Performed linguistic annotation, data analysis, and error identification for coverage improvements • Provided feedback as a peer reviewer to support multi-stage quality workflows • Trained and onboarded new linguists on annotation workflows and guideline standards

2019 - 2023

Education

Work History