Rohit Ravichandran

Key Skills

Software

Clickworker

Labelbox

Remotasks

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Image

Text

Video

Top Label Types

Evaluation Rating

Fine Tuning

Function Calling

Object Detection

Prompt Response Writing SFT

Question Answering

Text Generation

Text Summarization

Transcription

Freelancer Overview

I have over six months of hands-on experience in AI data labeling, evaluation, and training, working on production-scale systems through platforms such as Alignerr and Outlier AI. My work focuses on high-precision evaluation of large language model outputs, including reasoning quality, instruction adherence, tool-use correctness, and robustness. I regularly apply structured rubrics and ground-truth standards to assess complex, technical prompts, identify hallucinations and logical failure modes, and provide actionable feedback that directly supports model fine-tuning and alignment. What sets me apart is my strong technical background in computer science, AI, and cybersecurity, which allows me to deliver consistently high-signal annotations on challenging, code-centric and domain-specific tasks. I have experience designing evaluation criteria, benchmarking multiple model variants, and flagging systematic weaknesses that impact real-world deployment. In parallel, my research work on AI robustness, concept drift, and adversarial behavior strengthens my ability to evaluate model performance under edge cases and adversarial conditions, making my contributions particularly valuable for training reliable, production-ready AI systems.

Labeling Experience

AI Data Labeler & Model Evaluator – Outlier AI

Scale AITextEvaluation Rating

Annotated and evaluated AI outputs across complex technical prompts for consistency and quality. Ensured high signal quality using structured rubrics and detailed benchmarks. Provided iterative feedback to assist in model refinement and improvement. • Focused on prompt-based evaluation of AI-generated text • Performed detailed labeling for technical task assessment • Collaborated in ongoing model enhancement cycles • Maintained strict data quality standards throughout

Annotated and evaluated AI outputs across complex technical prompts for consistency and quality. Ensured high signal quality using structured rubrics and detailed benchmarks. Provided iterative feedback to assist in model refinement and improvement. • Focused on prompt-based evaluation of AI-generated text • Performed detailed labeling for technical task assessment • Collaborated in ongoing model enhancement cycles • Maintained strict data quality standards throughout

2024

AI Evaluation & Development Programmer – Alignerr

LabelboxTextEvaluation Rating

Evaluated and labeled AI model outputs for correctness, reasoning quality, and safety. Designed structured evaluation rubrics and ground-truth examples for use in model assessment. Identified systematic failure modes and edge cases to improve AI robustness. • Conducted rubric-based review of AI-generated text outputs • Focused on model reasoning and safety evaluation • Developed ground-truth data examples • Worked collaboratively on model improvement initiatives

Evaluated and labeled AI model outputs for correctness, reasoning quality, and safety. Designed structured evaluation rubrics and ground-truth examples for use in model assessment. Identified systematic failure modes and edge cases to improve AI robustness. • Conducted rubric-based review of AI-generated text outputs • Focused on model reasoning and safety evaluation • Developed ground-truth data examples • Worked collaboratively on model improvement initiatives

2024

Education

Work History