AI Data Quality & Engineering Lead

As an AI Data Quality & Engineering Lead, you will play a crucial role in ensuring top-tier data accuracy and integrity for large-scale AI/ML projects. Your responsibilities will include: - Leading and mentoring a team of Data Quality Analysts to achieve excellence in data quality. - Developing and refining quality assurance processes and metrics such as F1 score and inter-annotator agreement. - Analyzing quality trends, identifying root causes of issues, and deriving actionable insights. - Managing annotation and QA tools like Labelbox, Dataloop, and LabelStudio to optimize tool performance. - Driving automation and exploring new technologies to enhance operational efficiency. To qualify for this role, you should have: - 3+ years of experience in data quality management or related roles within AI/ML or data annotation environments. - Strong technical expertise with annotation tools and quality assurance methodologies. - Hands-on experience with data analysis tools like Excel, Python, and SQL. - Experience in managing remote or distributed teams. - Familiarity with AI/ML pipelines, data preparation, and model evaluation. - A passion for mentoring teams and driving continuous improvement in a fast-paced environment. Preferred qualifications include a background in agile tech environments, vendor-driven projects, and experience with prompt engineering or large-language-model-assisted workflows. If you are enthusiastic about leading a team dedicated to data excellence in the AI space and eager to work with cutting-edge technologies, we encourage you to apply for this exciting opportunity.,