Resume template · Updated October 2026
AI engineer resume template
Short answer
FAANGPath works well for an AI engineer: its labelled skill rows come before the jobs, so retrieval, evaluation and tool calling are read before a job title that may still say Software Engineer. The fictional sample builds LLM features into accounting software in London after three years of backend Python, with an assistant that cites sources, a graded test set, lower cost per answer and a prompt-injection review.
Download the AI engineer sample
Chloe is a fictional AI engineer. Replace every detail with your own before you send it.
The files are made in your browser by Resumity's own exporters, from this sample. The builder opens it as your own resume: click any line to replace it. Free plan, no watermark.
Why FAANGPath suits an AI engineer resume
FAANGPath is meant for technical candidates who want their tools read before their jobs. "AI engineer" covers very different work from one company to the next, so labelled rows naming retrieval, evaluation, tool calling and the serving stack tell a reader which kind you are at a glance. The black and grey page keeps attention on the bullets.
Second choice: Meridian. Meridian has the same order (summary, skills, experience, projects) with grey rules beside each heading instead of under it.
What goes in each section, in FAANGPath's order
FAANGPath prints its sections in a fixed order, the same in the PDF and the Word file. Here is that order for this sample, and what to put in each section.
- Professional Summary. Say what you build with language models and where it runs: "LLM features inside a production accounting product" is specific; "passionate about generative AI" is not. Mention the engineering background underneath, since teams hiring AI engineers want people who ship through tests and on-call.
- Skills. Technical holds languages and AI methods (retrieval-augmented generation, embeddings, evaluation, tool calling, prompt design). Tools holds the frameworks, vector stores, model APIs and tracing you use. Name approaches more than model versions; versions change every few months.
- Work Experience. Every AI bullet should answer three questions: how many people use it, how you know it works (a test set, graded answers, a regression caught), and what it costs or how fast it is. Add one bullet on safety, such as human confirmation before actions or input filtering.
- Projects. Open-source evaluation tools, a published retrieval demo with a write-up, or a contribution to a framework all give a reviewer something to read. Keep the project if it shows a skill your job does not.
- Education. One line. A computer science degree is common, but AI engineering hires heavily on shipped systems, so this section stays short.
The sample resume as text
The same content as the files above, so you can copy a line or compare it with your own.
Chloe Bennett
AI Engineer, LLM Applications
London, UK | chloe.bennett@example.com | 07700 900417 | github.com/sample-chloe-bennett | linkedin.com/in/sample-chloe-bennett
AI engineer building language model features into a production accounting product: retrieval over help content, tool calling, evaluation suites and cost controls. Came to AI from three years of backend Python, and ships through the same tests and on-call rotation as every other service.
Experience
AI Engineer, Ledgerwise, London, UK (05/2024 to Present)
- Built the in-app assistant that answers questions from 4,000 help articles using hybrid keyword and vector search over pgvector, with a cited source on every answer; it handles about 9,000 questions a week.
- Wrote a 600-question graded evaluation set that runs on every prompt or model change; it blocked two regressions before release.
- Added tool calling so the assistant can draft invoice reminders, with each action shown to the user to confirm before it runs.
- Cut average cost per answer by about 40 percent by routing simple questions to a smaller model and caching repeated retrievals.
- Ran a prompt-injection review of document upload against the OWASP Top 10 for LLM applications and added input filtering.
Software Engineer, Fernhill Systems, Manchester, UK (08/2021 to 04/2024)
- Built FastAPI services for bank statement import used by about 30,000 small businesses.
- Wrote the receipt-matching rules engine that later served as the baseline for a learned matcher.
- Joined the on-call rotation and wrote runbooks for the most common import failures.
Projects
evalbench (open source): A small Python library that runs graded LLM test sets in CI and posts a pass-rate diff on each pull request.
- Adopted by two other product teams at Ledgerwise.
Education
BSc, Computer Science, Pennine University (07/2021)
Skills
Python, TypeScript, Retrieval-augmented generation, Embeddings, LLM evaluation, Tool calling, Prompt design, FastAPI, PostgreSQL and pgvector, Elasticsearch, Hosted and open-weight LLM APIs, Docker, GitHub Actions, Writing design docs, Reviewing AI features with legal and support
How to make the sample yours
Report evaluations, not demos
Replace the sample's 600-question graded set with your own evaluation method: test set size, how answers are scored (by people, by rules, by a model) and one regression it caught. That is the line interviewers probe first.
Give usage, cost and latency
Questions a week, cost per answer and response time make an LLM feature look like a product rather than a prototype. Relative changes ("cost per answer down about 40 percent") are fine when totals are private.
Show the guardrails you built
Human confirmation before an action, citations, refusal handling, filtering of uploaded documents: name the ones you own. The OWASP Top 10 for LLM applications is a common reference if you ran a security review.
Keep the pre-AI engineering visible
Backend services, data pipelines or on-call experience show you can run software, which many AI prototypes never prove. Leave at least one earlier job with full bullets.
Be exact about training versus calling a model
Write "fine-tuned", "prompted", "distilled" or "served an open-weight model" as appropriate. Saying you trained a model when you called a hosted API will come out in the first technical interview.
Keywords to keep where they are true
Terms this kind of resume is usually searched for. Use the posting's exact wording, and only for work you have done.
- AI engineering
- large language models
- retrieval-augmented generation
- vector search
- embeddings
- LLM evaluation
- prompt design
- tool calling
- Python
- FastAPI
- pgvector
- prompt injection
- guardrails
- LLM observability
Mistakes to avoid
- A skills row naming every model, framework and agent library you tried over a weekend.
- "Built an AI chatbot" with no users, no evaluation and no cost figure.
- Dropping your earlier software or data work because it is not called AI.
- Claims about model accuracy with no description of how it was measured.
How the sample PDF reads in text extractors
We exported this sample with Resumity's PDF exporter in Chrome on 2026-10-29 and read the file back with three open-source text extractors. This shows whether software that turns a PDF into text can recover it; it is not a test of any commercial applicant tracking system. How we test templates.
| Extractor | Name, email, phone | Headings | Bullets intact |
|---|---|---|---|
| pdf.js 4.10.38 | All found | 5 of 5 | 9 of 9 |
| pdftotext (Xpdf) 4.00 | All found | 5 of 5 | 9 of 9 |
| pypdf 6.13.0 | All found | 5 of 5 | 9 of 9 |
Frequently asked questions
Is an AI engineer the same as a machine learning engineer?
Not usually. AI engineers mostly build applications on top of existing language models (retrieval, tools, evaluation), while ML engineers train and serve their own models. Use the title the posting uses and let your bullets show which work you do.
My title says Software Engineer but I build LLM features. What do I write?
Keep your official title on the job line and put "AI Engineer, LLM Applications" in the headline under your name. The bullets then show that the work matches the headline.
Should I list specific model names?
Sparingly. Mention a model family when it matters to the work, such as running an open-weight model yourself, but describe methods and results rather than versions, which date quickly.
What counts as an AI project if my employer does no AI work?
Something someone else can run or read: an evaluation tool, a retrieval demo on public documents with a write-up of what failed, or a merged contribution to an open-source framework.
How do I show I take AI safety and security seriously?
Name the controls you built: user confirmation before actions, citations, input filtering, red-team tests. If you reviewed against the OWASP Top 10 for LLM applications, say so; its first entry is prompt injection.
Will applicant tracking systems read this FAANGPath PDF?
The sample PDF read back with its contact details, all 5 section headings and all 9 bullets intact in three open-source text extractors (pdf.js, pdftotext and pypdf). Commercial systems such as Workday, Greenhouse or Taleo were not tested, and each employer configures its own.
Guides and templates for this resume
Sources, checked 29 October 2026
We opened every page below on that date. Pay, rules and requirements change, so check the source before you rely on a detail.
- OWASP Gen AI Security Project: 2025 Top 10 Risk and Mitigations for LLMs and Gen AI Apps (current 2025 list; LLM01:2025 is Prompt Injection)
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