Durgesh Ahirwar
Hi, I’m Durgesh Ahirwar, an AI Data Annotation Specialist based in Sagar, India.
About
Hi, I’m Durgesh Ahirwar, an AI Data Annotation Specialist based in Sagar, India. I curate high-quality training datasets for Computer Vision, NLP, and LLM fine-tuning—especially semantic segmentation, polygon labeling, object detection, video annotation, and audio/audio-linguistic transcription.
I love turning detailed guidelines into consistent ground-truth labels and improving dataset quality through QA checks like IoU-based validation and peer review, which helps boost inter-annotator agreement and reduce errors. Over my experience and freelancing work, I’ve contributed to thousands of labeled samples across modalities and I’m comfortable working fast in deadline-driven, remote teams.
- 2 years' experience
- Joined September 2026
Skills· 9
Languages
- HindiFluent
- EnglishAdvanced
Industry experience
- Computers & Electronics
- Education
- Retail
- Professional Services
- Media & Entertainment
- Software & Internet
Work experience· 7 roles
Orchestrated annotation of 700+ diverse samples per month (images, text, audio, documents) for multi-domain AI training. Contributed to cumulative dataset labeling across modalities and evaluated 20+ AI-generated responses with prompt-engineering feedback to improve model relevance. Maintained high data integrity through self-audits and strict guideline compliance, ranking in the top 3% of freelancers for quality and autonomy.
Rated relevance for 50+ map search queries and AI-personalized features monthly for the English (India) market, achieving a 96% consistency score. Provided feedback on cultural relevance and linguistic accuracy that contributed to measurable improvements in local user satisfaction. Consistently performed in the top 5% of raters against strict quality benchmarks.
Recorded and annotated 200+ hours of Hindi audio, including 500+ audio annotations, maintaining 99% precision for linguistic quality. Performed phonetic reviews and corrected 150+ transcription errors to enhance dataset quality. Followed linguistic guidelines for an AI model targeting Hindi-speaking users, achieving zero rework requests from the client due to careful adherence.
Education
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