Shoaib Rehman

Key Skills

Software

Appen

CrowdSource

Labelbox

Mindrift

OpenCV AI Kit (OAK)

Remotasks

Scale AI

Surge AI

Telus

Internal/Proprietary Tooling

AWS SageMaker

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Text

Top Task Types

Computer Programming Coding

Prompt Response Writing SFT

Question Answering

Segmentation

Translation Localization

Freelancer Overview

An AI Trainer and Data Annotation Specialist with a passion for building smarter AI through clean, context-aware data. Over the past few years, I’ve worked with platforms like Appen, Alignerr, and Invisible AI, where I evaluated AI responses, labeled data for computer vision models, and helped train large language models (LLMs) in both English and Hindi. I’ve been involved in everything from prompt evaluation and content scoring to code generation assessments and text alignment tasks. What sets me apart is my strong foundation in software development — I come from a full-stack Java background, so I understand how data drives real AI applications. I’ve labeled image and video datasets for self-driving cars and worked on projects that require precision, logic, and language nuance. I’m currently pursuing an M.Tech in AI from IISc Bangalore, which keeps me deeply connected to the latest in machine learning, NLP, and model training practices. I care about quality, clarity, and contributing to AI systems that work better for everyone.

Labeling Experience

Advanced Coding Trainer - Full Stack Development

Aws SagemakerComputer Code ProgrammingTranslation LocalizationFine Tuning

Led the development of robust backend APIs in Java to bridge AI models with production-ready applications, enabling seamless real-world deployment. Collaborated with NLP experts to integrate transformer-based models like BERT and GPT into Java-based microservices, ensuring performance, scalability, and modularity. Contributed to building custom internal annotation tools to streamline data labeling workflows, directly improving dataset quality. Focused on enhancing model performance by tuning algorithms and monitoring evaluation metrics, helping to close the loop between development and deployment. Additionally, I was hands-on in deploying these services using Docker and Kubernetes, and maintaining infrastructure on AWS SageMaker, supporting continuous delivery of AI-powered features.

Led the development of robust backend APIs in Java to bridge AI models with production-ready applications, enabling seamless real-world deployment. Collaborated with NLP experts to integrate transformer-based models like BERT and GPT into Java-based microservices, ensuring performance, scalability, and modularity. Contributed to building custom internal annotation tools to streamline data labeling workflows, directly improving dataset quality. Focused on enhancing model performance by tuning algorithms and monitoring evaluation metrics, helping to close the loop between development and deployment. Additionally, I was hands-on in deploying these services using Docker and Kubernetes, and maintaining infrastructure on AWS SageMaker, supporting continuous delivery of AI-powered features.

2021

Alignerr AI

LabelboxComputer Code ProgrammingFine TuningEvaluation Rating

As part of an AI model training and evaluation initiative, I reviewed and corrected AI-generated code in Java, JavaScript, and React, focusing on logic accuracy, code clarity, and industry best practices. I provided structured, example-driven feedback to guide model improvements and strengthen the AI’s ability to generate contextually correct and optimized code. My work also involved analyzing and refining datasets to reflect real-world development patterns, ensuring the model learned from high-quality examples. Additionally, I crafted and tested prompt variations to fine-tune the AI's performance across both frontend and backend coding scenarios.

As part of an AI model training and evaluation initiative, I reviewed and corrected AI-generated code in Java, JavaScript, and React, focusing on logic accuracy, code clarity, and industry best practices. I provided structured, example-driven feedback to guide model improvements and strengthen the AI’s ability to generate contextually correct and optimized code. My work also involved analyzing and refining datasets to reflect real-world development patterns, ensuring the model learned from high-quality examples. Additionally, I crafted and tested prompt variations to fine-tune the AI's performance across both frontend and backend coding scenarios.

2024 - 2025

Crowdgen by Appen

AppenTextQuestion AnsweringText Summarization

Worked on multilingual AI training projects focused on translating and localizing video and written content between English and Hindi, ensuring tone, cultural context, and accuracy were preserved. Leveraged AI-powered tools to transcribe speech and annotate language data, contributing to the improvement of generative and conversational models. Additionally, I reviewed and moderated social media content and ad campaigns, flagging violations of platform policies and ethical standards. Throughout the project, I provided detailed linguistic and contextual feedback to enhance AI output quality across NLP tasks.

Worked on multilingual AI training projects focused on translating and localizing video and written content between English and Hindi, ensuring tone, cultural context, and accuracy were preserved. Leveraged AI-powered tools to transcribe speech and annotate language data, contributing to the improvement of generative and conversational models. Additionally, I reviewed and moderated social media content and ad campaigns, flagging violations of platform policies and ethical standards. Throughout the project, I provided detailed linguistic and contextual feedback to enhance AI output quality across NLP tasks.

2024 - 2025

Education

Work History