AI-Powered Job Intake & Structuring Pipeline for HealthTech Platform

The client’s business development team received part-time medical job postings in highly inconsistent formats, requiring extensive manual effort to normalize and enter data into the platform.

• Job details arrived as PDFs, PowerPoint slides, spreadsheets, and photos of physical documents
• Manual transcription was time-consuming and error-prone
• Delays in publishing roles directly impacted time-to-fill for medical institutions

Any automation needed to maintain data accuracy, support human review, and integrate cleanly with the existing platform architecture.

• Medical job listings required high accuracy and structured validation
• Business users needed control before listings were published
• The solution had to integrate with an existing system that is developed by us

Built a custom AI-assisted ingestion pipeline that processes heterogeneous document inputs and converts them into structured job records using a fine-tuned language model.

• Upload pipeline accepts PDFs, presentations, spreadsheets, and images
• Custom prompts and a fine-tuned OpenAI model extract job details into a strict JSON schema
• Generated records are automatically created in the system and queued for human review

The client dramatically reduced manual workload while improving consistency and speed in job publication.

• Significantly shortened job posting turnaround time
• Improved data consistency across job listings
• Allowed business development staff to focus on partner relationships rather than data entry

Implemented as a modular pipeline that integrates AI services with the client’s existing backend.

• Python-based data pipeline with Pandas for data preparation and validation
• Fine-tuned OpenAI language model for structured job extraction
• JSON-based contract between the AI pipeline and the Ruby on Rails platform