Shukri I.

Key Skills

Software

Appen

Don't disclose

Mercor

Mindrift

Top Subject Matter

Computer vision training for technical diagrams (multimodal/visual reasoning)

Legal content validation using AI-assisted research (compliance and risk review)

Legal Services & Contract Review

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Segmentation

Question Answering

Text Generation

Text Summarization

Transcription

Evaluation/Rating

Prompt + Response Writing (SFT)

Freelancer Overview

AI Data Annotator (Multimodal) — Remote with Appen or Telus International. Brings 3+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Appen and Don't disclose. Education includes Juris Doctor, University of Arizona (James E. Rogers College of Law) and Bachelor of Science, University of Arizona (James E. Rogers College of Law). AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box, Evaluation, and Rating.

Labeling Experience

Associate Attorney / Legal Advisor — AI-assisted legal research and validation

Don't discloseText

Reviewed and drafted AI-assisted legal research outputs to ensure legal correctness and compliance with applicable standards. Performed quality-focused case documentation and prepared advisory materials based on research findings. Used AI tools as part of the research workflow, supporting validation and refinement of produced legal content. • Conducted comprehensive legal research using AI assistants (e.g., ChatGPT, grok, Gemini) alongside traditional legal databases. • Drafted legal memoranda, motions, contracts, and client advisories based on validated research content. • Reviewed and negotiated agreements by identifying risks and recommending safeguards. • Produced confidential case documentation with attention to correctness and regulatory alignment.

2024 - 2025

AI Data Annotator (Multimodal) — Remote with Appen or Telus International

AppenImageBounding Box

Structured multimodal dataset creation for computer vision model training, including detailed image annotations. Supported visual reasoning by producing step-by-step explanations for anomalies in diagrams used for model learning and evaluation. Collaborated with data science teams by reporting recurring model behavior to improve annotation and model quality. • Labeled image data at scale (50,000+ labeled data points) including image classification, bounding boxes, and polygon masks. • Produced chain-of-thought style logical explanations for visual anomalies within diagrams. • Communicated recurring hallucination patterns to data science teams to guide improvements. • Managed multiple simultaneous labeling tasks while maintaining high throughput and precision under deadlines.

2023 - 2024

Education

Work History