AI/ML Data Annotation and Training (self-driven and professional transition)
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Transitioned to involvement in AI/ML training workflows, focusing on data annotation, model evaluation, and RLHF pipelines. Contributed to data quality validation tasks and structured evaluation of LLM outputs for fine-tuning and supervised learning purposes. Engaged in prompt engineering, dataset curation, and assessment of model predictions for accuracy and relevance.
• Annotated and evaluated model outputs in text-based tasks.
• Performed data labeling for supervised learning and RLHF training tasks.
• Applied prompt engineering to optimize LLM behaviors and outputs.
• Ensured strict quality control through structured validation processes.
Transitioned to involvement in AI/ML training workflows, focusing on data annotation, model evaluation, and RLHF pipelines. Contributed to data quality validation tasks and structured evaluation of LLM outputs for fine-tuning and supervised learning purposes. Engaged in prompt engineering, dataset curation, and assessment of model predictions for accuracy and relevance. • Annotated and evaluated model outputs in text-based tasks. • Performed data labeling for supervised learning and RLHF training tasks. • Applied prompt engineering to optimize LLM behaviors and outputs. • Ensured strict quality control through structured validation processes.
2024 - Present