Sude O.
Ai Evaluator & Rlhf Specialist
Professional Value
Sude helps AI development teams ensure model safety and alignment. She applies rigorous Reinforcement Learning from Human Feedback and Language Quality Assurance to identify edge cases and minimise hallucinations. Specialising in frontier LLM evaluation, she designs scalable QA data pipelines that standardise the benchmarking of generative AI systems, ensuring meticulous performance tracking.
Selected Clients
Executive Profile
Sude is a Software Engineer and advanced AI Evaluator with thousands of hours of hands-on experience in Reinforcement Learning from Human Feedback (RLHF), highly equipped to drive model safety, accuracy, and alignment for development teams. Her journey in AI model optimisation began in 2022, where she focused on the rigorous evaluation of frontier Large Language Models (LLMs).
She consistently evaluates complex model outputs across diverse domains — from logical reasoning and software engineering to nuanced language tasks. Through strict Language Quality Assurance (LQA) and technical reviews, Sude actively identifies edge cases, documents subtle biases, and provides the highly structured RLHF necessary to correct model hallucinations and align outputs with human intent.
Holding a BSc in Software Engineering and pursuing Master's studies in Engineering Management, she brings a critical, systems-level approach to AI evaluation. Sude excels at developing scalable evaluation frameworks and quality benchmarks, and has successfully designed robust QA data pipelines that standardise the benchmarking of generative AI models — applying engineering optimisation principles and agile methodologies throughout.
The challenge of enhancing AI alignment requires not just an understanding of Generative AI, but rigorous, analytical, and highly structured execution. Sude has a proven track record of authoring domain-expert training data, along with the technical communication skills to collaborate seamlessly with global AI researchers. She is eager to make a direct, measurable impact on the next generation of advanced machine learning models.
Core Competencies
Ai - Artificial Intelligence
Sude specialises in Reinforcement Learning from Human Feedback (RLHF) and the evaluation of frontier Large Language Models (LLMs). She focuses on driving model safety, accuracy, and alignment through rigorous analysis. Her work involves identifying edge cases, documenting biases, and correcting hallucinations to ensure outputs meet human intent. She possesses a deep understanding of generative AI systems and the technical nuances required for effective model optimisation.
Quality Mgt
Sude's BSc in Software Engineering informs a systems-level approach to AI evaluation that goes beyond rating outputs. She has designed QA data pipelines and evaluation frameworks that bring consistency and rigour to generative AI benchmarking - applying agile methodologies to keep the process scalable as models evolve. Her Language Quality Assurance (LQA) reviews are technically grounded, targeting edge cases and subtle model behaviours that surface-level checks miss.