Recruiters search booleans, not keywords. When a hiring manager opens an ATS or a sourcing extension, they do not type a list of friendly concepts and hope for the best. They construct strings like python AND (django OR fastapi) NOT legacy. If your CV is a flat list of buzzwords separated by commas, it fails the query shape before a human ever looks at the page.
A single word passes a literal scan, but a project sentence passes a boolean. If you list Python in a skills section at the bottom, it sits in isolation. When the query looks for Python paired with specific framework context and concurrency constraints, the isolated keyword floats unattached. It has no proximity score to the terms that matter.
Write the stack inside the sentence where you used it, at the scale you used it. Instead of a detached bullet point for PostgreSQL, write about migrating a million rows of session data in PostgreSQL under strict latency limits. Now the nouns and verbs sit within five words of each other. They trigger proximity operators in automated searches.
Optimize for the query shape, not the word list. Think about how a domain expert narrows down a pile of fifty candidates to five. They filter by exclusion, mandate intersections, and require specific version markers. Build your experience notes to match those distinct logical branches so you survive the initial trim.
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