Sudeer

Key Skills

Software

Appen

Mindrift

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Label Types

RLHF

Evaluation Rating

Computer Programming Coding

Data Collection

Transcription

Freelancer Overview

B.Tech Electronics & Communication Engineering graduate with Data Analyst certification specializing in data labeling/annotation (Python, SQL, Power BI). Experienced in precise data labeling, annotation workflows, predictive modeling, and quality assurance for AI training datasets. Designed IoT ECG device requiring accurate real-time data labeling/annotation for healthcare monitoring and remote transmission. Excelled in robot control programming (IRC, ROS) involving meticulous data annotation for automation processes. Proven expertise in data cleaning, labeling consistency, requirements validation, and technical annotation. Multilingual (English, Telugu, Hindi) with exceptional attention to detail for high-quality AI data labeling. Ready for remote data labeling/annotation roles ensuring training data accuracy and scalability.

Labeling Experience

Data annotation

MindriftComputer Code ProgrammingRLHFEvaluation Rating

AI Data Labeling Specialist at Mindrift (Jan 2024 - Present), specializing in RHLF (Reinforcement Learning from Human Feedback) and LLM fine-tuning datasets. Created 10,000+ high-quality prompt-response pairs using RHLF methodology for LLM alignment, including preference annotations and reward modeling. Performed supervised fine-tuning (SFT) data labeling with classification, entity recognition (NER), and evaluation/rating tasks across healthcare and robotics domains. Used Mindrift platform for scalable annotation workflows, achieving 97% inter-annotator agreement on complex reasoning and safety-critical LLM training data.

AI Data Labeling Specialist at Mindrift (Jan 2024 - Present), specializing in RHLF (Reinforcement Learning from Human Feedback) and LLM fine-tuning datasets. Created 10,000+ high-quality prompt-response pairs using RHLF methodology for LLM alignment, including preference annotations and reward modeling. Performed supervised fine-tuning (SFT) data labeling with classification, entity recognition (NER), and evaluation/rating tasks across healthcare and robotics domains. Used Mindrift platform for scalable annotation workflows, achieving 97% inter-annotator agreement on complex reasoning and safety-critical LLM training data.

2024 - 2025

Education

Work History