Hardik Singh

Key Skills

Software

Other

Appen

Labelbox

Lionbridge

OneForma

Mindrift

SuperAnnotate

iMerit

Top Subject Matter

LLM Evaluation

Prompt Engineering

RLHF Pipelines

Top Data Types

Text

Image

Computer Code Programming

Top Task Types

Fine Tuning

Classification

Segmentation

Object Detection

Text Generation

Question Answering

Text Summarization

RLHF

Red Teaming

Evaluation Rating

Prompt Response Writing SFT

Computer Programming Coding

Bounding Box

Transcription

Freelancer Overview

Analyst – AI/LLM Practice. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Engineering, Chandigarh University (2025) and Higher Secondary Certificate, Adarsh Vidya Mandir (2021). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

Analyst – AI/LLM Practice

Text

As an Analyst – AI/LLM Practice, I performed advanced LLM evaluation, prompt optimization, and response quality analysis across multiple AI workflows. I worked on RLHF pipelines including SxS evaluation, grounding analysis, and hallucination detection to improve AI system reliability. I analyzed structured and unstructured data outputs and translated findings into actionable feedback for model improvement. • Evaluated LLM outputs using structured frameworks and SQL concepts. • Conducted SxS evaluation and grounding analysis to detect AI hallucinations. • Debugged AI responses through logs, API traces, and logical reasoning. • Collaborated with cross-functional teams to resolve data, model, and system issues.

As an Analyst – AI/LLM Practice, I performed advanced LLM evaluation, prompt optimization, and response quality analysis across multiple AI workflows. I worked on RLHF pipelines including SxS evaluation, grounding analysis, and hallucination detection to improve AI system reliability. I analyzed structured and unstructured data outputs and translated findings into actionable feedback for model improvement. • Evaluated LLM outputs using structured frameworks and SQL concepts. • Conducted SxS evaluation and grounding analysis to detect AI hallucinations. • Debugged AI responses through logs, API traces, and logical reasoning. • Collaborated with cross-functional teams to resolve data, model, and system issues.

2026 - Present

AI Data Annotator

AppenTextClassification

As an AI Data Annotator, I labeled and categorized text, audio, and search relevance datasets for supervised model training. I performed intent classification, search quality evaluation, and maintained high annotation accuracy aligned with guidelines. My responsibilities included structured data analysis and feedback for improving annotation consistency. • Labeled diverse datasets including text and audio for training NLP models. • Conducted search relevance and intent classification tasks. • Applied SQL-based logic for structured evaluation tasks. • Provided annotation feedback and met productivity benchmarks.

As an AI Data Annotator, I labeled and categorized text, audio, and search relevance datasets for supervised model training. I performed intent classification, search quality evaluation, and maintained high annotation accuracy aligned with guidelines. My responsibilities included structured data analysis and feedback for improving annotation consistency. • Labeled diverse datasets including text and audio for training NLP models. • Conducted search relevance and intent classification tasks. • Applied SQL-based logic for structured evaluation tasks. • Provided annotation feedback and met productivity benchmarks.

2025 - 2025

Computer & Information Systems Specialist – AI Trainer

OtherTextFine Tuning

As an AI Trainer, I trained and fine-tuned large language models using high-quality annotations and structured feedback. I analyzed outputs to identify edge cases, biases, and system-level errors in LLMs. My efforts supported model improvement and enhanced response accuracy via SxS evaluation and grounding analysis. • Conducted side-by-side evaluations for prompt/responses and error localization. • Identified and annotated biases, edge cases, and hallucinations in AI outputs. • Used logical debugging and structured analysis to provide actionable insights. • Documented model behaviors and communicated findings to engineering teams.

As an AI Trainer, I trained and fine-tuned large language models using high-quality annotations and structured feedback. I analyzed outputs to identify edge cases, biases, and system-level errors in LLMs. My efforts supported model improvement and enhanced response accuracy via SxS evaluation and grounding analysis. • Conducted side-by-side evaluations for prompt/responses and error localization. • Identified and annotated biases, edge cases, and hallucinations in AI outputs. • Used logical debugging and structured analysis to provide actionable insights. • Documented model behaviors and communicated findings to engineering teams.

2025 - 2025

Data Annotator

TelusTextClassification

As a Data Annotator, I annotated and validated datasets to support machine learning development workflows. I evaluated AI-generated responses for safety, compliance, and quality, performing quality assurance checks to minimize errors. I worked within a global remote team adhering to strict data handling standards. • Annotated and validated diverse datasets for ML pipelines. • Evaluated response quality using log and output analysis. • Ensured data compliance and accuracy through rigorous checks. • Reduced system inaccuracies via error pattern analysis.

As a Data Annotator, I annotated and validated datasets to support machine learning development workflows. I evaluated AI-generated responses for safety, compliance, and quality, performing quality assurance checks to minimize errors. I worked within a global remote team adhering to strict data handling standards. • Annotated and validated diverse datasets for ML pipelines. • Evaluated response quality using log and output analysis. • Ensured data compliance and accuracy through rigorous checks. • Reduced system inaccuracies via error pattern analysis.

2024 - 2025

Education

Work History