Amaan Shamim Khan

Key Skills

Software

Data Annotation Tech

Labelbox

Label Studio

Scale AI

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Bounding Box

Computer Programming Coding

Data Collection

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I am a technical data annotation and AI training specialist with a strong software engineering background and hands-on experience supporting large language model (LLM) evaluation, text annotation, and structured data review. My work involves assessing model outputs for correctness, reasoning quality, and adherence to specifications, as well as annotating and validating text, code, and structured datasets used in AI training pipelines. I bring a strong understanding of NLP concepts, programming logic, and data quality standards to ensure accurate and reliable training data. In addition to annotation, I have contributed to improving AI training workflows through automation, testing, and technical documentation. My experience working in Agile, remote environments has strengthened my ability to follow detailed guidelines, provide consistent evaluations, and deliver high-quality results at scale. With a focus on precision, clarity, and technical accuracy, I am well suited for complex AI training tasks involving LLM evaluation, code-related data, and advanced text annotation.

Labeling Experience

AI Data Quality Automation & Annotation Pipeline Optimization

Scale AITextRLHFFine Tuning

Contributed to AI training projects focused on improving data quality, consistency, and scalability of annotation pipelines. Responsibilities included validating annotated text and structured datasets, identifying quality gaps, and applying classification and evaluation standards across large volumes of training data. Developed and maintained Python-based automation modules to support preprocessing, validation, and post-annotation checks, reducing manual effort and improving overall data reliability. Collaborated with distributed teams in Agile workflows, followed strict quality metrics, and ensured adherence to annotation guidelines to support fine-tuning and reinforcement learning workflows.

Contributed to AI training projects focused on improving data quality, consistency, and scalability of annotation pipelines. Responsibilities included validating annotated text and structured datasets, identifying quality gaps, and applying classification and evaluation standards across large volumes of training data. Developed and maintained Python-based automation modules to support preprocessing, validation, and post-annotation checks, reducing manual effort and improving overall data reliability. Collaborated with distributed teams in Agile workflows, followed strict quality metrics, and ensured adherence to annotation guidelines to support fine-tuning and reinforcement learning workflows.

2024

LLM Evaluation & Technical Text Annotation for AI Training

Scale AITextText GenerationRLHF

Worked on large-scale AI training initiatives focused on improving the performance and reliability of large language models. Responsibilities included evaluating and rating model-generated responses for accuracy, reasoning quality, completeness, and alignment with task instructions. Performed comparative analysis and ranking of multiple LLM outputs as part of RLHF workflows.

Worked on large-scale AI training initiatives focused on improving the performance and reliability of large language models. Responsibilities included evaluating and rating model-generated responses for accuracy, reasoning quality, completeness, and alignment with task instructions. Performed comparative analysis and ranking of multiple LLM outputs as part of RLHF workflows.

2023 - 2024

Education

Work History