Amir Q.

Key Skills

Software

Mercor

Top Subject Matter

AI training data preparation

LLM prompt evaluation and alignment testing

Data pipeline ingestion and preparation from documents and structured files

Top Data Types

Text

Document

Top Task Types

Prompt + Response Writing (SFT)

Data Collection

Freelancer Overview

Data Engineer in Contract Review, Compliance, and Legal Research. Brings 14+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Mercor, Internal, and Proprietary Tooling. Education includes Bachelor of Science, The Hebrew University of Jerusalem (2026). AI-training focus includes data types such as Text and Document and labeling workflows including Prompt + Response Writing (SFT) and Data Collection.

Labeling Experience

Data Engineer – Social services institute (2026–Present)

DocumentData Collection

Built and managed data pipelines to digitize and prepare data for downstream research and governmental use. Received and consolidated source materials including PDFs, books, and CSV/SQL/Excel data into stored datasets for analysis and publication. Performed data ingestion and storage steps that support creating structured data from unstructured documents. • Ingesting heterogeneous data sources (PDFs, books, CSV, SQL, Excel) • Transforming/standardizing inputs into database-ready formats • Managing pipelines for reliable dataset creation • Supporting research and reporting via stored structured data

2026 - Present

AI Trainer at Alignerr and Mercor (2023–Present)

MercorTextPrompt + Response Writing (SFT)

Executed precision data annotation and labeling for large-scale datasets to support AI model workflows. Crafted and refined complex prompts to evaluate AI model outputs with the goal of reducing hallucinations and improving alignment with human intent. Identified and documented edge cases during model testing and analyzed error trends for underlying issues. • Annotating/labeling dataset examples for training or evaluation • Creating and iterating prompts used to test model behavior • Evaluating outputs for alignment and hallucination reduction • Performing root-cause analysis on error trends

2023 - Present

Education

Work History