Jubrilla A.

Key Skills

Software

Don't disclose

Top Subject Matter

AI evaluation and LLM data annotation (AML/KYC, finance and compliance)

Data governance and compliance data quality for AI training/evaluation

Legal Services & Contract Review

Top Data Types

Text

Document

Top Task Types

Fine-tuning

Data Collection

Freelancer Overview

AI Generalist — Trainer, Evaluator & Data Annotator (Outlier AI). Brings 12+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Don't disclose. Education includes Master of Arts, Erasmus University Rotterdam (ISS) (2024) and Master of Business Administration, LAUTECH (2019). AI-training focus includes data types such as Text and Document and labeling workflows including Fine-tuning and Data Collection.

Labeling Experience

AI Generalist — Trainer, Evaluator & Data Annotator (Outlier AI)

Don't discloseTextFine-tuning

Evaluated and rated AI-generated outputs across domains such as finance, compliance, and governance, focusing on factual accuracy, reasoning quality, and response safety. Annotated and labeled complex datasets used to train and fine-tune large language models, supporting improvements in model reliability and output quality. Applied prompt engineering to stress-test model behavior by identifying edge cases, hallucinations, and alignment failures for use in RLHF pipelines. • Assessed AI responses for quality and safety • Contributed labeled training data for LLM improvement • Performed prompt-based robustness and failure analysis • Used domain expertise in AML/KYC to guide high-stakes evaluation

2024 - Present

Project & Research Officer (NABC) — Data Governance & AI training support

Don't discloseDocumentData Collection

Governed regulatory and operational data across 30+ African market-entry programmes to ensure compliance and audit-ready reporting for EU-funded initiatives. Supported AI training and evaluation efforts through structured governance workflows and data quality remediation practices. Coordinated stakeholders across policy, operations, and data teams to enable consistent, traceable data used for programme delivery and reporting. • Ensured regulatory-aligned data governance and audit readiness • Improved data consistency via governance workflows and templates • Enabled cross-team coordination for data quality and reporting • Supported data quality remediation for downstream AI/evaluation use

2024 - 2025

Education

Work History