Phase 9: Career & Portfolio · 50 min · Python · LinkedIn · ATS keyword matching

Resume & LinkedIn for AI Security Roles

89% of AI security postings require prompt injection experience — if your resume doesn't say it in the first six lines, the ATS never shows it to a human.

Hiring signal: Recruiters and applicant tracking systems for AI Security Engineer, AI Red Team Engineer, and DevSecOps for AI Pipelines roles filter on specific keywords (OWASP LLM Top 10, prompt injection, Garak, PyRIT, MITRE ATLAS) before a human ever reads a resume. Candidates who mirror the language of the posting and quantify impact clear the ATS filter and the six-second human scan that follows it.

What you will learn

Rewrite a plain description of red team or guardrail work into a quantified, ATS-aligned resume bullet
Identify which of the 8 AI security job roles and salary bands a given resume is best positioned for
Structure a LinkedIn headline, About section, and Featured section around AI security hiring signals
Use a scoring heuristic to catch vague bullets before a recruiter does

The Problem

AI security engineering job postings grew 124% year-over-year, and most of them are written by security or engineering leads who list very specific requirements: OWASP LLM Top 10, prompt injection and jailbreak defense, Garak or PyRIT, MITRE ATLAS, NIST AI RMF. Before a human recruiter ever opens your resume, most companies run it through an applicant tracking system that scores it against those exact terms. A resume that describes real work in vague, generic language — "worked on AI safety," "contributed to security initiatives" — can lose to a weaker candidate whose resume simply uses the right words.

This isn't a call to keyword-stuff. It's the opposite problem candidates usually have: they did the work (built a detector, ran a red team engagement, hardened a guardrail pipeline) but describe it the way they'd describe it to a friend, not the way the posting describes the requirement. The fix is mechanical — mirror the vocabulary of the role, then prove it with a number.

The eight roles this course targets — AI Security Engineer ($150k–$210k), AI Red Team Engineer ($160k–$230k), DevSecOps for AI Pipelines ($150k–$210k), AI Security Architect ($170k–$250k), AI Governance & Compliance Engineer ($140k–$200k), LLM Application Security Engineer ($150k–$220k), Adversarial ML Researcher/Engineer ($170k–$280k), and AI Security Consultant ($160k–$240k) — each have a distinct keyword fingerprint. A resume that could be for any of them signals for none of them clearly.

The six-second scan is real
Recruiter studies consistently show an initial resume scan lasts six to eight seconds. In that window, a human is looking for a job title match, 2-3 recognizable tool/framework names, and whether the bullets have numbers in them. Everything else — your education, your soft skills, your career narrative — gets read only if that six-second scan passes. Optimize the first third of your resume for that scan, not for a thorough read that may never happen.

Framing Red Team and Guardrail Work as Resume Bullets

The formula that works, bullet by bullet: strong verb + specific tool/technique + quantified result. Compare:

Weak (what most candidates write)	Strong (what clears the ATS and the scan)
"Worked on prompt injection stuff for the team's chatbot project."	"Built a 3-layer prompt injection detector (regex + heuristic scoring + DeBERTa classifier) achieving a 94% detection rate at 3% false-positive rate across a 240-example adversarial test set."
"Responsible for security reviews of agent codebases."	"Audited a multi-agent RAG system for tool privilege escalation, identifying 6 attack vectors and reducing critical findings from 4 to 0 after remediation."
"Familiar with red teaming tools like Garak and PyRIT."	"Ran a Garak + PyRIT red team engagement against a RAG chatbot, documenting 9 findings and cutting the indirect-injection attack chain success rate from 41% to 6% after fixes."

Notice the pattern: the weak version describes proximity to the work ("worked on," "familiar with," "responsible for"). The strong version describes the work itself and its measured outcome. If you completed this course's projects, you have the numbers already — detection rates, findings counts, before/after percentages are in your own lab reports from Phases 2, 4, 6, and 8. Use the real ones.

For each of your four course projects, you should be able to write one bullet in this shape:

Phase 2 (injection detector + jailbreak library): detection rate, false-positive rate, number of techniques in your library, bypass rate reduction
Phase 4 (agent security audit): number of attack vectors identified, severity breakdown, before/after finding counts
Phase 6 (red team engagement): number of Garak probes / PyRIT orchestrators run, findings count, attack chain success rate before and after remediation
Phase 8 (production security system): number of security layers implemented, detection/response time, compliance frameworks mapped

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers LinkedIn: Headline, About, Featured, What Hiring Managers Scan For First, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Prompt Injection