Dharmendra Raj

Key Skills

Software

Mercor

Mindrift

OneForma

Telus

Other

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Computer Programming/Coding

Data Collection

Fine-tuning

Prompt + Response Writing (SFT)

Freelancer Overview

I am an AI and Machine Learning practitioner with strong experience in training, evaluating, and optimizing AI systems. Over the past few years, I have worked on projects involving cryptocurrency and stock price prediction, healthcare diagnostics, and natural language processing. My expertise includes building time-series forecasting models (LSTM), AI-driven financial risk analysis, and medical data classification systems, where I contributed to both data preprocessing/labeling and model evaluation to ensure high-quality outputs. In addition, I have hands-on experience in LLM evaluation, prompt engineering, and fine-tuning, having optimized prompts that significantly improved language model accuracy and usability. I am skilled in using Python, SQL, Tableau, PowerBI, and cloud platforms (GCP) for data handling, analysis, and visualization. My ability to combine technical skills with domain knowledge in finance, NLP, and healthcare AI allows me to contribute effectively to diverse AI training and data labeling tasks.

Labeling Experience

AI Data Annotation & Translation Support Project

OtherTextText SummarizationTranslation Localization

Worked on AI training for multilingual datasets, focusing on translation/localization, text summarization, and evaluation of AI outputs. Annotated and validated healthcare-related text datasets, ensuring compliance with medical terminology standards. Supported function-calling annotation tasks for LLM integration with external tools. Delivered consistent, high-quality annotations across tens of thousands of entries, contributing to LLM fine-tuning and safe deployment in healthcare and general AI applications. Maintained quality metrics above 97% throughout the project.

Worked on AI training for multilingual datasets, focusing on translation/localization, text summarization, and evaluation of AI outputs. Annotated and validated healthcare-related text datasets, ensuring compliance with medical terminology standards. Supported function-calling annotation tasks for LLM integration with external tools. Delivered consistent, high-quality annotations across tens of thousands of entries, contributing to LLM fine-tuning and safe deployment in healthcare and general AI applications. Maintained quality metrics above 97% throughout the project.

2024

AI Content Evaluation & Annotation for NLP Systems

TelusTextEntity Ner ClassificationText Generation

Contributed to AI training datasets for NLP systems by performing text annotation, summarization, and NER labeling. Evaluated AI responses for grammar, fluency, and factual correctness. Conducted emotion recognition and context-based classification to refine dialogue models. Collaborated with multilingual datasets, ensuring accuracy and consistency across large volumes of data. Delivered projects on tight deadlines with 98%+ quality compliance scores, enabling TELUS Digital’s global AI training initiatives.

Contributed to AI training datasets for NLP systems by performing text annotation, summarization, and NER labeling. Evaluated AI responses for grammar, fluency, and factual correctness. Conducted emotion recognition and context-based classification to refine dialogue models. Collaborated with multilingual datasets, ensuring accuracy and consistency across large volumes of data. Delivered projects on tight deadlines with 98%+ quality compliance scores, enabling TELUS Digital’s global AI training initiatives.

2024

LLM Prompt Engineering & Evaluation Project

OtherTextQuestion AnsweringText Generation

Worked on optimizing and evaluating Large Language Models (LLMs) by designing and refining 50+ prompts using zero-shot and few-shot learning. Tasks included evaluating AI responses for correctness, coherence, and safety, performing A/B testing, and scoring responses using perplexity and alignment measures. Contributed to 40+ hours of evaluation and annotation work that improved text generation quality by 30% and NLP accuracy by 25%. Maintained strict quality control by adhering to evaluation rubrics, consistency checks, and alignment guidelines.

Worked on optimizing and evaluating Large Language Models (LLMs) by designing and refining 50+ prompts using zero-shot and few-shot learning. Tasks included evaluating AI responses for correctness, coherence, and safety, performing A/B testing, and scoring responses using perplexity and alignment measures. Contributed to 40+ hours of evaluation and annotation work that improved text generation quality by 30% and NLP accuracy by 25%. Maintained strict quality control by adhering to evaluation rubrics, consistency checks, and alignment guidelines.

2024

Education

Work History