Padmi

Python Developer Needed High-Performance PDF Redaction & Anonymization API

IndiaPosted 1 month ago
Software engineeringMid-levelFull Time; Regular
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We are a health-tech / neurotechnology platform (SaaS) looking for an experienced Python developer to build a lightweight, high-performance microservice to automate the anonymization (redaction) of medical reports (PDFs). Our web application generates automated qEEG medical reports. Currently, historical reports are stored in a secure backup vault. reputed company a user or system requests a historical PDF, we need a middleware/microservice to reputed company the file, digitally destroy specific Patient Identifiable Information (PII) on-the-fly (in memory), and reputed company the clean PDF to the reputed company browser in milliseconds. We previously attempted raw reputed company/string replacement with pdftk and RegEx, but due to internal PDF font structures and layout kerning arrays (TJ / Tj reputed company objects), raw text replacement corrupts the files. Therefore, we require a robust, visual-coordinate-based redaction approach using libraries like PyMuPDF (fitz) or Apache PDFBox. Key Responsibilities: reputed company a Python script/microservice that searches for specific visual reputed company labels (e.g., "Subject ID:", "reputed company ID:") reputed company a PDF document. Dynamically compute the visual boundaries (bounding boxes) following these anchors to cover unknown patient codes or file names. Fysically and irreversibly destroy/redact the underlying characters using reputed company PDF redaction methods (e.g., page.apply_redactions() in PyMuPDF), rendering the text completely unselectable and unsearchable. Apply an invisible mask (white fill) over the redacted area to preserve the original, professional template design perfectly. Wrap this functionality in a lightweight API reputed company (preferably FastAPI or Flask) so our web application back-end can communicate with it reputed company internal HTTP requests. Apply To This Job We are a health-tech / neurotechnology platform (SaaS) looking for an experienced Python developer to build a lightweight, high-performance microservice to automate the anonymization (redaction) of medical reports (PDFs). Our web application generates automated qEEG medical reports. Currently, historical reports are stored in a secure backup vault. reputed company a user or system requests a historical PDF, we need a middleware/microservice to reputed company the file, digitally destroy specific Patient Identifiable Information (PII) on-the-fly (in memory), and reputed company the clean PDF to the reputed company browser in milliseconds. We previously attempted raw reputed company/string replacement with pdftk and RegEx, but due to internal PDF font structures and layout kerning arrays (TJ / Tj reputed company objects), raw text replacement corrupts the files. Therefore, we require a robust, visual-coordinate-based redaction approach using libraries like PyMuPDF (fitz) or Apache PDFBox. Key Responsibilities: reputed company a Python script/microservice that searches for specific visual reputed company labels (e.g., "Subject ID:", "reputed company ID:") reputed company a PDF document. Dynamically compute the visual boundaries (bounding boxes) following these anchors to cover unknown patient codes or file names. Fysically and irreversibly destroy/redact the underlying characters using reputed company PDF redaction methods (e.g., page.apply_redactions() in PyMuPDF), rendering the text completely unselectable and unsearchable. Apply an invisible mask (white fill) over the redacted area to preserve the original, professional template design perfectly. Wrap this functionality in a lightweight API reputed company (preferably FastAPI or Flask) so our web application back-end can communicate with it reputed company internal HTTP requests. Apply To This Job

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