AI Data Quality Architect

1 day ago

Ayala Alabang, National Capital Region, Philippines beBeeData Full time $120,000 - $150,000
Job Title: AI Data Quality Architect

 

Job Description:

The AI data quality architect will be responsible for developing and implementing high-quality annotation workflows to ensure AI models are built and evaluated on reliable data without compromising speed or efficiency.

This role exists to address the critical need for integrity and accuracy of testing, training, and evaluation data as AI systems scale rapidly across industries.

Key Responsibilities include:

  • Building and guiding a high-performing team: Lead and mentor data quality analysts, set clear quality goals, provide feedback, and foster a culture of precision and accountability.
  • Ensuring quality at scale: Develop and refine robust QA processes, SOPs, and statistical quality metrics (e.g., F1 score, inter-annotator agreement) to protect annotation integrity.
  • Driving transparency and insight: Create dashboards and reports that reveal quality trends, root causes of errors, and improvement opportunities; communicate insights to leadership and clients.
  • Championing tool innovation and efficiency: Manage annotation and QA platforms (e.g., Labelbox, Dataloop, LabelStudio) and evaluate or implement new automation tools to elevate efficiency and maintain quality.
Required Skills and Qualifications:
  • 3+ years in data quality management, data operations, or related AI/ML or data annotation roles.
  • Proven experience designing and executing QA strategies for large-scale, multi-modal data annotation projects.
  • Leadership experience managing remote or distributed teams.
  • Deep understanding of data annotation processes, QA methodologies, and metrics (e.g., F1 score, inter-annotator agreement).
  • Strong data-analysis skills and ability to translate findings into actionable recommendations.
  • Excellent communication skills with experience presenting data and quality insights to technical and non-technical stakeholders.
  • Proficiency with annotation and QA tools (e.g., Labelbox, Dataloop, LabelStudio).
  • High proficiency in Excel and Google Sheets; familiarity with Python and SQL.
  • Familiarity with AI/ML pipelines concepts including data prep, model training, and evaluation.

 

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