AI Data Trainer & Analyst

Job Description Job Title: AI Data Trainer & Analyst Job Type: Full-time or Part-time (Contract) Location: Remote Job Summary: Join our customer's team as an AI Data Trainer & Analyst to play a pivotal role in enhancing cutting-edge generative AI and NLP models. You will curate, annotate, and evaluate large-scale datasets, contributing to the advancement of artificial intelligence capabilities. This position suits analytical thinkers passionate about data quality, innovation, and AI-driven solutions. Key Responsibilities: • Curate, label, and annotate large text, image, and video datasets to train and evaluate AI/ML models. • Design and implement prompt engineering strategies to optimize generative AI outputs. • Create comprehensive annotation guidelines and perform rigorous quality assurance audits. • Develop representative dataset samples and execute meticulous sampling and curation processes. • Identify and mitigate bias, ensuring fairness and inclusivity across datasets. • Utilize SQL, Python (pandas), and spreadsheet tools for data analysis, reporting, and process improvement. • Document workflows, results, and insights with exceptional written and verbal communication skills. Required Skills and Qualifications: • Proven experience in data labeling, annotation, and quality assurance within AI/ML projects. • Expertise in prompt engineering, linguistic analysis, and guideline creation. • Strong command of sampling, dataset curation, and bias/fairness detection methodologies. • Hands-on skills in image, video, and text annotation tasks for AI model development. • Foundational proficiency in SQL and Python (pandas) for data processing and analytics. • Advanced capability with Excel or Google Sheets for data manipulation and reporting. • Outstanding communication, documentation, and organizational abilities. Preferred Qualifications: • Familiarity with LLMs (Large Language Models), text classification, summarization, and content moderation. • Previous involvement in AL (Active Learning) training projects. • Experience with diverse dataset modalities and multi-lingual annotation tasks.