Building Synthetic Data Pipelines

I design scalable, reproducible data pipelines for LLM training and alignment—covering collection, filtering, labeling, augmentation, and versioning.

What you’ll get:

1. Data sourcing & taxonomy design
2. Cleaning, deduplication & quality scoring
3. Instruction / preference data generation
4. Human-in-the-loop workflows
5. Dataset versioning & auditability

Best for:

Teams struggling with noisy data, inconsistent results, or slow iteration.