Hello, I’m Pavlo Dragan, a Ph.D.-level Political Scientist and Linguist with over 19 years of experience in high-stakes text analysis. I now focus on Generative AI Alignment and Reinforcement Learning from Human Feedback (RLHF), with a specialization in geopolitical domain analysis, bias mitigation, and evaluating factual grounding for large language models.
Beyond academia, I provide Tier-3 expert evaluation of model outputs to ensure they are grounded, coherent, and free from hallucinations. My track record includes data annotation and translation workflows with 98%+ accuracy, multilingual reasoning (Ukrainian, Russian, English), and adversarial prompt design to stress-test AI safety in geopolitical contexts.
As a translator, with over 19 years of experience in linguistics , I have handled a diverse range of content types, ensuring high accuracy and cultural relevance:
Technical & Digital: Software/UI localization, web content, mobile apps, and technical documentation (specializing in Agrotech).
Legal & Political: Legal contracts, academic papers in social sciences, and high-level political analysis (specializing in the Post-Soviet region).
Multimedia: Audiovisual content, including subtitling and video localization (840+ videos completed).
AI & LLM Training Data: Prompt-response pairs, RLHF (Reinforcement Learning from Human Feedback) datasets, fact-checking reports, and safety/style evaluation for Large Language Models.
Marketing & Creative: Transcreation for premium brands (e.g., Swarovski, Google), business communications, and marketing collateral.
Highly Regulated: Life sciences and corporate compliance materials (e.g., Johnson & Johnson, Bio-Rad).
Multimedia & Audiovisual: Extensive experience in subtitling, time-coding, and video localization (840+ videos localized). I handle everything from creative transcreation for marketing videos to technical subtitling for Agrotech and legal seminars.
AI-Assisted Media: Skilled in Machine Translation Post-Editing (MTPE) for subtitles and automated transcripts, ensuring linguistic naturalness and technical precision (SDH/CC standards).
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