Senior AI Engineer, Data Quality And Pipeline Automation

You will support AI training work focused on data pipeline quality automation. The work uses data pipelines, execution profiles, and warehouse datasets to help AI systems learn from structured expert review and clear written feedback.

You will review source materials and model outputs, apply project guidelines, explain your decisions, and keep your judgments consistent across repeated tasks.

• Evaluate model outputs against project rubrics.
• Review source materials and outputs for quality and guideline compliance.
• Document evidence-based judgments clearly.
• Explain decisions so feedback can improve model behavior.
• Maintain consistent quality across repeated review tasks.

The project details do not specify a pay rate. This is listed as an entry-level contract role, and prior AI training experience is not required.

• Schedule: 40 hours per week.
• Time alignment: At least 6 hours aligned with IST each day.
• Workload: 20+ hours per week.
• Language: Professional fluency in English.
• Required technical background: LLM and agent engineering, including tool calling, multi-step agents, and frameworks such as the Claude Agent SDK or LangGraph.
• Required focus: Safety, evaluation, and reliability for AI agents.
• Helpful background: Data quality, observability, and statistics.
• Helpful tools: Great Expectations, Soda, or Monte Carlo.
• Helpful knowledge: Anomaly detection, time-series baselines, and forecasting.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind modern AI systems, including reviewing model responses, applying quality guidelines, and writing feedback. People with engineering and data expertise are needed because reliable judgments help models produce safer, more accurate results.