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Prompt + Response Writing (SFT)
Bounding Box
Entity (NER) ClassificationAI Trainer / Prompt Engineer (Junior). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other, Internal, and Proprietary Tooling. Education includes Master of Computer Applications, N/A (2025) and Bachelor of Computer Applications, N/A (2024). AI-training focus includes data types such as Text and Image and labeling workflows including Prompt + Response Writing (SFT), Bounding Box, and Entity (NER) Classification.
"The core objective was to reduce model training cycle times, improve infrastructure reliability, and enable AI teams to iterate rapidly without manual DevOps bottlenecks
Conducted language and localization annotation including NER, transcription, and translation review in Odia, Hindi, and English. Assessed translation quality, performed text classification, and tagged entities for building robust multilingual datasets. Reviewed and corrected grammatical errors and regional dialect usage for precise AI dataset development. • Evaluated and improved dataset QA for South Asian language models. • Performed entity tagging across localization projects and language QA tasks. • Provided transcription and translation review ensuring cultural nuance awareness. • Supported sentiment labeling and content moderation for multilingual datasets.
Performed bounding box annotation, semantic segmentation, and image classification on diverse visual datasets. Leveraged detail-oriented analysis skills developed in incident triage to ensure high accuracy and consistency. Followed established annotation quality guidelines while handling edge cases. • Applied inter-annotator agreement checks for annotation quality. • Utilized segmentation tagging and binary image labeling protocols. • Assisted in object recognition, classification, and data review for computer vision tasks. • Managed annotation across multiple projects using internal tools.
Wrote, critiqued, and ranked AI-generated responses in English, Hindi, and Odia for instruction-following, prompt evaluation, and output scoring. Detected failures in instruction following, hallucinations, and other quality gaps, while documenting findings in clear reports. Applied a systematic approach to validate text outputs in language datasets for multilingual AI projects. • Conducted RLHF and evaluation rating for multilingual large language models. • Performed classification and ranking of textual outputs for prompt engineering tasks. • Engaged in instruction following assessment to ensure high-quality responses. • Developed clear technical documentation summarizing annotation findings.
Bachelor of Computer Applications, Computer Applications
Site Reliability Engineer
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