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Freelancer Overview
I bring hands-on experience to the AI training data lifecycle, specializing in RLHF, SFT, workflows to improve model alignment and accuracy. My core strength lies in translating complex, ambiguous annotation guidelines into high-fidelity training inputs across text, code. Having successfully led a labeling project / met strict quality KPIs, I focus heavily on reducing edge-case ambiguity and ensuring rigorous data quality, directly contributing to measurable improvements in model safety and performance. What sets me apart is my ability to bridge the gap between technical guidelines and human intuition. I am highly proficient in [mention key skills like prompt engineering, semantic analysis, error taxonomy creation and experienced with industry-standard platforms like Toloka. By combining sharp analytical precision with a deep understanding of what makes high-quality training data, I deliver the reliable, meticulously structured datasets required to train enterprise-grade AI models.