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Appen
Label StudioMercorTolokaTelusOneFormaClickworkeriMeritNo subject matter listed
Audio
Computer Code Programming
ImageOver the past year, I have been working on AI data labeling and evaluation projects on platforms such as Appen, CrowdGen, Hive Micro, Toloka and similar annotation marketplaces, focusing mainly on text-based tasks, content quality checks, and instruction-following evaluations. This builds on my software development background (C++, C#, Python), giving me strong analytical thinking, attention to detail, and comfort working with structured guidelines, complex rubrics, and edge cases in datasets. I regularly work with LLMs, prompts, and chat agents, so I am used to judging model responses for relevance, safety, tone, and hallucinations, and then adjusting instructions to improve output quality. This combination of coding skills, familiarity with AI tools, and hands-on data labeling experience allows me to understand both how models work and what high-quality training data should look like, which helps me deliver consistent, accurate annotations for AI training projects.
Classify the logo in the given picture based on whether logo matches the branded logo or whether it is a normal picture.
We need to analyze the audio output of LLMs and evaluate the LLM's output based on various factors. Generating rubrics and evaluating the response of the LLM
To classify whether a webpage is junk or not. the level of classification depends on various criteria like whether the webpage is first loaded or not, if loaded- how is the main content aligned. based on this, we will classify whether this webpage is junk or not
Bachelor of Engineering, Electronics and Communication
Higher Secondary Certificate, Higher Secondary Education
Senior Technical Lead
Software Engineer
