Rauhan Ahmed

Key Skills

Software

Label Studio

Prodigy

Roboflow

CVAT

Top Subject Matter

Llms Domain Expertise

Generative AI

Retrieval-Augmented Generation

Top Data Types

Text

Document

Image

Top Task Types

Prompt Response Writing SFT

Classification

Freelancer Overview

AI Evaluation & Data Engineer. Brings 4+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Label Studio, Prodigy, and Roboflow. Education includes Bachelor of Technology, Oriental Institute of Science & Technology (2025) and Higher Secondary Certificate, Bal Bhawan School (2021). AI-training focus includes data types such as Text and Document and labeling workflows including Prompt + Response Writing (SFT) and Classification.

Labeling Experience

AI Evaluation & Data Engineer

Label StudioTextPrompt Response Writing SFT

Led the curation and annotation of high-quality prompt-response pairs for LLM fine-tuning and agentic workflows. Evaluated RAG workflows through benchmark dataset creation and relevance annotation to improve system accuracy. Automated data validation and labeling pipelines, ensuring rigorous QA/QC for LLM fine-tuning datasets. • Focused on factual consistency, tone alignment, and hallucination reduction. • Mentored interns on labeling standards and prompt optimization. • Used Label Studio and internal/proprietary tooling for annotations. • Achieved 25% increase in contextual relevance and 40% reduction in deployment time.

Led the curation and annotation of high-quality prompt-response pairs for LLM fine-tuning and agentic workflows. Evaluated RAG workflows through benchmark dataset creation and relevance annotation to improve system accuracy. Automated data validation and labeling pipelines, ensuring rigorous QA/QC for LLM fine-tuning datasets. • Focused on factual consistency, tone alignment, and hallucination reduction. • Mentored interns on labeling standards and prompt optimization. • Used Label Studio and internal/proprietary tooling for annotations. • Achieved 25% increase in contextual relevance and 40% reduction in deployment time.

2024 - Present

Machine Learning Data Specialist

ProdigyDocumentClassification

Managed accurate labeling of large-scale financial datasets, focusing on time-series anomaly detection for ML models. Collaborated on pipeline design to clean, annotate, and process real-time financial data streams. Established ground truth for time-series classification, contributing to significant portfolio improvements. • Utilized Prodigy and DVC for annotation and dataset versioning. • Coordinated with domain experts for label taxonomy definition. • Built dashboards to monitor annotation progress. • Enabled signal detection and reduced portfolio drawdown by 70%.

Managed accurate labeling of large-scale financial datasets, focusing on time-series anomaly detection for ML models. Collaborated on pipeline design to clean, annotate, and process real-time financial data streams. Established ground truth for time-series classification, contributing to significant portfolio improvements. • Utilized Prodigy and DVC for annotation and dataset versioning. • Coordinated with domain experts for label taxonomy definition. • Built dashboards to monitor annotation progress. • Enabled signal detection and reduced portfolio drawdown by 70%.

2023 - 2024

Generative AI Data Engineer

RoboflowTextPrompt Response Writing SFT

Curated and annotated conversational datasets to train and benchmark enterprise-grade RAG chatbots. Managed annotation guidelines for text and image data to ensure high inter-annotator agreement. Integrated automated data quality checks into inference pipelines for robust evaluation. • Led bounding box and segmentation annotation for virtual try-on GenAI models. • Used Roboflow, CVAT, and internal tools for labeling tasks. • Authored comprehensive annotation guides for diverse modalities. • Enabled a 30% increase in virtual try-on accuracy.

Curated and annotated conversational datasets to train and benchmark enterprise-grade RAG chatbots. Managed annotation guidelines for text and image data to ensure high inter-annotator agreement. Integrated automated data quality checks into inference pipelines for robust evaluation. • Led bounding box and segmentation annotation for virtual try-on GenAI models. • Used Roboflow, CVAT, and internal tools for labeling tasks. • Authored comprehensive annotation guides for diverse modalities. • Enabled a 30% increase in virtual try-on accuracy.

2022 - 2023

Education

Work History