AI Security Engineer Resume Examples & Writing Guide
Show how you attacked the model before someone else did, and what you changed so it stayed fixed
By CraftMyDocs Editorial · Updated October 5, 2026 · How these guides are produced
Professional Summary Example
"AI Security Engineer with 7 years in application and cloud security and 2 years focused on LLM systems. Built the red-team programme for a customer-facing agent platform: 1,800 automated attack cases and monthly manual exercises uncovered 23 high-severity issues, including indirect prompt injection through retrieved documents, all remediated before general availability. Introduced tool-permission scoping and output filtering that cut successful jailbreaks in testing from 19% to under 2%. Experienced in threat modelling against OWASP LLM Top 10 and MITRE ATLAS, and in explaining residual risk to legal and product leaders."
Typical US Salaries Across Technology Roles
Industry-wide range, not a AI Security Engineer-specific figure. Use it to sense-check an offer, then look up this exact title on the BLS Occupational Outlook Handbook or a salary survey for your region.
- Basis
- Industry-wide estimate for Technology roles
- Region · currency
- United States · USD, annual base pay
- Source
- Estimated US national base-pay ranges, reviewed 2026-08. Actual pay varies by location, employer and specialization.
Top Skills to Put on Your Resume
Recruiters and ATS scanners look for these exact skills on AI Security Engineer resumes:
Best Action Verbs for AI Security Engineer
Open each AI Security Engineer bullet with one of these verbs — they match how Technology postings describe the work:
AI Security Engineer Experience Bullet Point Repository
Select a category and click Copy Bullet to paste directly into your resume.
Built an automated adversarial suite of 1,800 cases covering jailbreaks, prompt injection and data exfiltration; run in CI for each model or prompt release.
Led monthly manual red-team exercises on a customer-facing agent, surfacing 23 high-severity findings before general availability.
Introduced least-privilege tool permissions and human approval for irreversible actions; the number of reproducible harmful tool calls in testing fell from 14 to 0.
Added output filtering and content isolation for retrieved documents, reducing successful indirect injection from 19% to under 2% on the benchmark.
Deployed PII detection and redaction in the retrieval path, stopping sensitive fields reaching prompts and logs.
Established model provenance checks (hash verification, safetensors only, approved registries) and scanned third-party model artefacts for unsafe serialisation.
Mapped systems to NIST AI RMF and ISO/IEC 42001 controls for customer security reviews.
Wrote the AI incident response runbook and ran two tabletop exercises with legal and support.
Weak vs. Strong Bullet Example
"Responsible for llm red teaming & adversarial testing and other tasks as assigned."
"Built an automated adversarial suite of 1,800 cases covering jailbreaks, prompt injection and data exfiltration; run in CI for each model or prompt release."
Top Resume Mistakes for AI Security Engineer Applicants
"Skilled in llm red teaming & adversarial testing" is a claim any applicant can make. Show it with a result instead: "Built an automated adversarial suite of 1,800 cases covering jailbreaks, prompt injection and data exfiltration; run in CI for each model or prompt release."
Applicant tracking systems match wording literally. If the posting says "LLM Red Teaming & Adversarial Testing", "Prompt Injection & Jailbreak Defence", "OWASP Top 10 for LLM Applications", use those exact phrases — not a synonym you prefer.
An ATS reads text, not graphics — a five-dot bar next to "Guardrails (Llama Guard, NeMo Guardrails, Presidio)" is invisible to it. Write each one as plain text in a Skills line, and name it again in the AI Security Engineer bullet where you used it.
AI Security Engineer ATS Optimization Checklist
- Headline: Your title line reads "AI Security Engineer" (or the posting's exact title), not a creative variant.
- Keywords present: LLM Red Teaming & Adversarial Testing, Prompt Injection & Jailbreak Defence, OWASP Top 10 for LLM Applications, MITRE ATLAS & NIST AI RMF, Model & Data Supply-Chain Security — each one appears at least once in Skills or Experience.
- Verbs first: Bullets open with AI Security Engineer verbs such as Red-teamed, Threat-modelled, Hardened, Gated.
- File Format: Send a PDF (or DOCX if the posting asks) without password protection, named like
FirstName-LastName-AI-Security-Engineer-Resume.pdf. - Standard Headings: Use "Work Experience", "Education" and "Skills" — and split Skills into Tools (Guardrails (Llama Guard, NeMo Guardrails, Presidio), Garak, PyRIT & Promptfoo); Technical (LLM Red Teaming & Adversarial Testing, Prompt Injection & Jailbreak Defence, OWASP Top 10 for LLM Applications); Professional (Risk Translation for Product & Legal Teams).
- Font & Margins: Use 10-12pt standard fonts (Inter, Arial, Roboto) with 0.5 to 1 inch margins.
Complete AI Security Engineer Career & Writing Guide
As companies connect language models to customer data, internal tools and the open web, the attack surface moves with them. A support agent that can issue refunds, a coding assistant with repository access or a retrieval system that indexes confidential documents can all be steered by text that an attacker controls. AI Security Engineers exist to find those weaknesses first and design systems where a successful attack does limited damage.
The role combines classic security engineering with a growing body of AI-specific practice: red teaming models and agents, designing guardrails and permission boundaries, securing the model and data supply chain, and aligning with frameworks such as the OWASP Top 10 for LLM Applications, MITRE ATLAS and the NIST AI Risk Management Framework. It is new enough that standard resume templates do not capture it. This guide shows how to present attack-and-defend work with measurable results, how to talk about frameworks without name-dropping, and how to describe confidential findings responsibly so a hiring panel sees depth and discretion together.
1. How to Write a Professional Summary
Because this is an emerging specialty, your summary must show both halves of the title: security depth and AI-specific fluency.
- Security base: years in application, cloud or product security, which establishes credibility with security managers.
- AI focus: how long you have worked on LLM or ML systems and what kind: agents, retrieval, model hosting or governance.
- Result: a quantified finding or improvement, such as issues discovered before launch or a drop in successful jailbreaks.
- Frameworks: one phrase naming the standards you work against, for example OWASP LLM Top 10, MITRE ATLAS and NIST AI RMF.
Good example: 'Built the red-team programme for an agent platform; 23 high-severity issues found and fixed before general availability.' Avoid broad claims like 'securing the future of AI' and avoid presenting yourself as an ethics commentator unless that is the role. Hiring managers for this position want to know that you can break things methodically and that your fixes hold. If your AI-specific work is recent, say so honestly and lead with a project that shows it, because candidates with a long track record in this exact title barely exist.
2. Highlighting Your Work Experience
Write bullets that show an attack, a mitigation and a verified result. Organise by the surface you protected.
Red teaming and testing
- “Built an automated adversarial suite of 1,800 cases covering jailbreaks, prompt injection and data exfiltration; run in CI for each model or prompt release.”
- “Led monthly manual red-team exercises on a customer-facing agent, surfacing 23 high-severity findings before general availability.”
Design and controls
- “Introduced least-privilege tool permissions and human approval for irreversible actions; the number of reproducible harmful tool calls in testing fell from 14 to 0.”
- “Added output filtering and content isolation for retrieved documents, reducing successful indirect injection from 19% to under 2% on the benchmark.”
- “Deployed PII detection and redaction in the retrieval path, stopping sensitive fields reaching prompts and logs.”
Supply chain and platform
- “Established model provenance checks (hash verification, safetensors only, approved registries) and scanned third-party model artefacts for unsafe serialisation.”
Governance and response
- “Mapped systems to NIST AI RMF and ISO/IEC 42001 controls for customer security reviews.”
- “Wrote the AI incident response runbook and ran two tabletop exercises with legal and support.”
Always name the measure that proves the fix, not only the fix.
3. Selecting the Right Skills
Pair classic security skills with AI-specific ones, grouped so a reader sees both quickly.
- Core security: threat modelling (STRIDE), application and API security, cloud security, identity and secrets management, penetration testing, incident response.
- AI attack techniques: prompt injection (direct and indirect), jailbreaking, data extraction, training-data poisoning, model inversion and extraction, adversarial examples, agent tool abuse.
- Testing tools: Garak, PyRIT, Promptfoo, custom attack harnesses in Python, evaluation datasets for harmful content.
- Defences: guardrail models such as Llama Guard, NeMo Guardrails, input and output filtering, Presidio for PII, rate limiting, sandboxing and permission scoping for tools.
- Supply chain: model artefact scanning, dependency and container scanning, signing and provenance, safe model formats.
- Frameworks: OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, Google SAIF where applicable.
- Engineering: Python, PyTorch familiarity, Kubernetes, CI/CD, logging and tracing.
Soft skills belong in bullets: explaining residual risk to a product lead, negotiating a launch gate or running a cross-team tabletop. Be careful with framework names; list the ones you have applied to a real system. If you hold publications, advisories or open-source contributions, give them their own line because they stand out in a small field.
4. Education, Licenses & Certifications
Most AI Security Engineers hold a bachelor's degree in computer science, cybersecurity, mathematics or a related field. A master's or PhD in machine learning or security can help for research-oriented teams, particularly those studying adversarial machine learning, but many product security teams hire on practical evidence.
List degrees with institution, field and year. Include research or thesis work if it relates to adversarial robustness, privacy-preserving machine learning, secure systems or NLP safety.
Credentials and activities that strengthen the application:
- Security certifications: CISSP, OSCP, GIAC or a cloud security specialty, which signal baseline competence.
- AI training: short courses on adversarial machine learning, LLM security or responsible AI from reputable providers. List issuer and year.
- Public work: a blog post or write-up on an attack you reproduced, CTF challenges focused on AI, bug bounty reports on AI products, or merged contributions to red-team tools.
- Talks and papers: workshop papers, conference talks and meetup presentations.
If you are early in your career, build one project that shows the full loop: choose an open model or a small agent, attack it systematically, add defences, and measure what changed. Present it with a repository link and a clear results table summary in two bullets.
5. Layout & ATS Formatting Rules
In a specialty this young, clarity and credibility matter more than design.
- Length: one page up to about ten years of combined experience; two pages for senior or principal candidates with several programmes.
- Order: Summary, Skills, Experience, Research and public work, Education, Certifications.
- Framing line: under each role, one line describing the AI systems in scope: agent platform, retrieval assistant, hosted model service, number of models or users.
- Bullets: use the attack, control and measured result pattern. Four to six per role.
- Confidentiality: describe vulnerability classes and counts, never exploit steps or customer data. Follow your employer's disclosure rules.
- Layout: single column, standard headings, no graphics. Make sure the file reads cleanly as plain text.
- Keywords: AI security, LLM security, red teaming, prompt injection, adversarial machine learning, threat modelling, OWASP, MITRE ATLAS, NIST AI RMF, guardrails.
- Links: GitHub, write-ups, talks and advisories as plain URLs.
Ask a security colleague to read it for overclaiming. In this field, reviewers quickly separate people who have tested real systems from those who have read the frameworks.
Frequently Asked Questions
What does an AI Security Engineer do differently from a traditional security engineer?
An AI Security Engineer protects systems whose behaviour is probabilistic and steered by text. Beyond classic controls such as identity, network and application security, you test for prompt injection, data leakage through retrieval, insecure tool use by agents, training-data poisoning, model theft and unsafe outputs. Your resume should show a bridge: established security practice (threat modelling, penetration testing, secure design) applied to AI-specific attack surfaces, with named frameworks like OWASP LLM Top 10, MITRE ATLAS and NIST AI RMF.
Is there a certification for AI security I should list?
No single credential dominates yet. Established security certifications such as CISSP, OSCP, GIAC or cloud security specialties remain the strongest signals, and AI-focused training from reputable providers can supplement them. Rather than relying on a certificate, show practice: public red-team write-ups, contributions to tools like Garak or PyRIT, CVEs or disclosed vulnerabilities in AI systems, conference talks, and documented evaluation results. State the training issuer and year if you list any, and keep it below your hands-on achievements.
How can I describe red-team findings without exposing my employer's weaknesses?
Describe the class of vulnerability, the testing method, the volume and the outcome, without naming the product or revealing exploit details. For example: 'Discovered indirect prompt injection via retrieved web content in an agent workflow; implemented content isolation and tool allow-lists, closing all 7 reproduced attack paths.' Counts, severity levels and time to remediate are acceptable. Check your employer's disclosure policy before publishing detail, and use public frameworks as the common language.
How do I move into AI security from application security or ML engineering?
From application security, add depth in how models and agents fail: run a published LLM attack suite against a system you build, write up your findings and map them to OWASP LLM categories. From ML engineering, build security fundamentals: threat modelling, identity, secrets and incident response. On your resume, make the transferable base explicit, add one project that shows adversarial testing with measured results, and state in your summary which side of the intersection you are growing from.