Spots 
Short answer: extract positioned text with a real PDF parser, normalize it without destroying evidence, ask a model for typed candidate fields, and render the shareable document from a template your SaaS team owns. Keep the source PDF private. For a B2B hiring workflow, the least complex safe result is a new redacted document, never a cosmetically covered copy of the original.
Choose template ownership first. It decides where personal data can leak, who can change the output, and how reliably you can test redaction.
The diagram in words is short: private PDF bytes enter an extraction worker; ordered text leaves it; a model proposes a candidate record; deterministic validation accepts or rejects fields; an owned template receives only the approved subset. Logs receive identifiers and counts, not resume content. Which team should own the sharing template?
Pick application ownership when redaction is a security boundary. This fits a multi-tenant B2B SaaS product that sends candidate summaries to interviewers or customers. The renderer accepts a narrow type instead of an open bag of resume fields. A template change then passes through code review, fixtures, and deployment beside its disclosure policy.
Pick operations ownership when layout changes are frequent. Give the editor named placeholders such as candidateLabel and skills, not arbitrary access to the parsed record. Publish versions and run leakage tests against each version before activation. The flexibility is useful, but the review burden is real.
Recipient-owned templates make sense when contractual formats vary by customer. Treat them as untrusted input. Reject unknown placeholders, disable remote resources, and render in an isolated worker. If a customer asks for email in an anonymous profile, fail closed.
The decision rule is blunt: the team accountable for disclosure owns the field allowlist, even when another team owns the visual layout. How can I parse a PDF resume and extract text for a model?
A PDF is a page description, not a promise of reading order. Text can arrive as positioned fragments, while scanned pages may have no useful text layer. Use a conforming PDF implementation for byte parsing, hidden behind a small interface. Keep parser-specific objects out of the privacy-sensitive service.
Require the parser adapter to return page number and coordinates. Coordinates help reconstruct lines, detect repeated headers, and show evidence during review. They also expose a concrete trap: sorting by y alone can interleave two columns.
Normalize conservatively. Preserve page breaks. Collapse repeated spaces inside a line, but do not lowercase names or rewrite punctuation before extraction.
Short answer: extract positioned text with a real PDF parser, normalize it without destroying evidence, ask a model for typed candidate fields, and render the shareable document from a template your SaaS team owns.