AI Training & Data Annotation Specialist
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RLHFWorked on AI training and data annotation workflows focused on improving large language model (LLM) performance through reinforcement learning from human feedback (RLHF). Responsibilities included evaluating AI-generated responses, reviewing prompt-response quality, assessing contextual relevance, identifying inaccuracies, and supporting data quality improvement processes for conversational AI systems. Performed text annotation, response ranking, content classification, prompt evaluation, and quality review tasks across large-scale text datasets while maintaining high accuracy and consistency standards. Contributed to AI training pipelines by analyzing language quality, factual alignment, safety compliance, tone consistency, and user intent understanding. Worked extensively with structured and unstructured text data in fast-paced digital review environments requiring strong analytical thinking, attention to detail, internet research capabilities, and adherence to quality assurance guidelines. Supported scalable AI workflows through efficient annotation practices, structured evaluation methods, and feedback-driven review processes.