AI document processing pipeline
Fine-tuned classification and extraction models for high-volume document ingestion. Identified a training-data quality regression and architected the remediation path.
- Client
- Confidential — fintech
- Role
- AI infrastructure engineer
- Year
- 2025
- Stack
- OpenAI fine-tuningPythonAWSS3Step Functions
End-to-end ownership of an LLM-powered document processing pipeline: classification, extraction, validation, and human-review escalation.
What we did
- Built the fine-tuning data pipeline (curation, deduplication, validation against ground truth).
- Stood up an evaluation harness so model regressions were visible before deploys, not after.
- Diagnosed a quality regression that turned out to be label drift in the training set, not the model. Wrote up the post-mortem and the data-quality controls that prevent it from recurring.
- Migrated the production inference path from a fragile single-region setup to a queueable, retry-safe pipeline on Step Functions.
Outcome
Measurable accuracy lift on the priority document classes. Clear, repeatable retraining cadence. The team stopped firefighting model regressions.