Ladline Noronha

Key Skills

Software

Appen

Labelbox

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Bounding Box

Classification

Data Collection

Text Summarization

Translation Localization

Freelancer Overview

I bring 10+ years of experience in operations, logistics, and data-driven process management, with growing expertise in AI training data and annotation workflows. While my prior roles were centered on business operations, I’ve worked extensively with structured datasets, CRM systems, and digital platforms like Salesforce CPQ, Oracle, JIRA, and Microsoft Dynamics 365—giving me a solid foundation in the structured, detail-oriented tasks essential to data labeling. My work often involved organizing high volumes of customer, logistics, and product data, ensuring precision, consistency, and compliance, which are directly aligned with annotation quality control for machine learning models. In projects like the IndRAA RMA Alignment, I leveraged JIRA to automate tagging and tracking, streamlining repair data classification—similar in structure and intent to AI annotation pipelines. I also led SOP development and trained teams on standardizing data documentation, skills vital to maintaining annotation accuracy across large datasets. My strengths in pattern recognition, data categorization, and process consistency make me well-suited for contributing to high-quality AI training sets. I am excited to apply these capabilities in the evolving AI space, particularly in a mission-driven organization like OpenTrain AI that values open, transparent model development.

Labeling Experience

Text Data Evaluation Specialist – Outlier.ai LLM Audit Project

OtherTextEntity Ner ClassificationClassification

Worked as a data evaluator and annotation specialist for a project focused on auditing and refining training data for large language models using Outlier.ai. Responsibilities included evaluating model-generated responses, rating prompt-output quality, and tagging issues related to factuality, helpfulness, tone, and safety. Contributed to NER labeling, text summarization accuracy reviews, and classification of intent behind user prompts to improve training feedback loops. The project required careful attention to instruction adherence and linguistic nuance, covering over 40,000 annotated text pairs across multiple domains. I participated in guideline calibration sessions, performed inter-rater reliability assessments, and used Jira to log anomalies and QA observations.

Worked as a data evaluator and annotation specialist for a project focused on auditing and refining training data for large language models using Outlier.ai. Responsibilities included evaluating model-generated responses, rating prompt-output quality, and tagging issues related to factuality, helpfulness, tone, and safety. Contributed to NER labeling, text summarization accuracy reviews, and classification of intent behind user prompts to improve training feedback loops. The project required careful attention to instruction adherence and linguistic nuance, covering over 40,000 annotated text pairs across multiple domains. I participated in guideline calibration sessions, performed inter-rater reliability assessments, and used Jira to log anomalies and QA observations.

2023 - 2024

AI Annotation & Quality Control Specialist – Labelbox

LabelboxTextEntity Ner ClassificationClassification

Contributed to a multi-phase annotation project involving high-quality training data for LLM fine-tuning and evaluation. Using Labelbox, I performed detailed entity recognition, classification, and prompt-response alignment on large volumes of customer queries and internal documentation. I managed ontology setup, followed strict QA protocols, and collaborated with reviewers to resolve edge cases and ambiguous data. In the Outlier.ai project phase, I was responsible for anomaly detection and validation tasks, ensuring data integrity through careful flagging and scoring of mislabeled or low-confidence samples. The project covered over 50,000+ text entries and 5,000+ images, with accuracy targets above 98%. I was also involved in developing and adhering to annotation SOPs, benchmarking guidelines, and weekly audit reports to maintain consistency across annotators.

Contributed to a multi-phase annotation project involving high-quality training data for LLM fine-tuning and evaluation. Using Labelbox, I performed detailed entity recognition, classification, and prompt-response alignment on large volumes of customer queries and internal documentation. I managed ontology setup, followed strict QA protocols, and collaborated with reviewers to resolve edge cases and ambiguous data. In the Outlier.ai project phase, I was responsible for anomaly detection and validation tasks, ensuring data integrity through careful flagging and scoring of mislabeled or low-confidence samples. The project covered over 50,000+ text entries and 5,000+ images, with accuracy targets above 98%. I was also involved in developing and adhering to annotation SOPs, benchmarking guidelines, and weekly audit reports to maintain consistency across annotators.

2023 - 2024

Education

Work History