Jeremiah A.

Key Skills

Software

Remotasks

Appen

Top Subject Matter

LLM training dataset evaluation for code generation (Python/JavaScript).

LLM prompt/response training data preparation and refinement.

Legal Services & Contract Review

Top Data Types

Text

Document

Top Task Types

Prompt + Response Writing (SFT)

Freelancer Overview

AI Technical Contributor & Analyst | Remotasks / Appen / Atlas (LLM dataset evaluation). Brings 11+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Remotasks and Appen. Education includes Bachelor of Laws, University of Embu and Certificate, Citizen College (2023). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation, Rating, and Prompt + Response Writing (SFT).

Labeling Experience

AI Technical Contributor & Analyst | Appen (LLM dataset preparation support)

AppenTextPrompt + Response Writing (SFT)

Contributed to AI data work by generating and refining prompt/response examples as part of LLM dataset preparation. Focused on producing technically grounded content that supports model learning for developer-oriented queries. Used feedback loops to improve the quality and usefulness of training examples. • Prepared or revised prompts and corresponding responses. • Ensured responses aligned with expected technical behavior. • Supported iterative dataset improvement cycles. • Used QA-style feedback to reduce failure modes.

2016 - Present

AI Technical Contributor & Analyst | Remotasks / Appen / Atlas (LLM dataset evaluation)

Remotasks

Evaluated and refined code-generation datasets used to train Large Language Models, concentrating on the technical correctness of Python and JavaScript outputs. Applied structured review to identify errors and improve dataset quality for downstream model performance. Performed consistency checks against benchmark expectations for international stakeholders. • Assessed technical accuracy of generated code. • Flagged edge cases in AI logic and provided structured feedback. • Managed high-priority data pipelines with reported 98%+ accuracy. • Contributed reliability improvements for technical query handling.

2016 - Present

Education

Work History