AI Engineer CV 2026: Template, Skills & LLM Portfolio

Last updated: 3 August 2026 · 15 min read

The AI engineer CV 2026 candidates need is being read twice before a recruiter opens it — once by the classic ATS, once by the LLM ranking layer sitting on top. The bar has moved: generic Python-and-TensorFlow templates from 2023 no longer clear either filter. Frontier labs like OpenAI and Anthropic and every enterprise ML team above 200 engineers now expect production LLM deployment evidence, an MLOps stack that includes Kubernetes and MLflow, and a portfolio section with clickable GitHub, Hugging Face and deployed-demo links. This guide covers exactly what to put on your CV, how to structure each section for both filters, the 2026 tech stack recruiters scan for, and a full worked AI engineer CV example you can adapt.

TL;DR

• An AI engineer CV in 2026 needs 6 sections in this order: contact + portfolio links, 2-3 sentence summary with one flagship metric, skills grouped by family, experience with quantified bullets, projects with live URLs, and education.
• The 2026 tech stack recruiters scan for: Python + one systems language, PyTorch, LLM stack (Transformers, RAG, LangChain/LangGraph, vLLM, evaluation), MLOps (Kubernetes, Docker, MLflow, Airflow, Terraform), cloud (AWS/GCP/Azure), and data (Spark, SQL).
• Portfolio is a full section, not a footnote. GitHub stars, Hugging Face model downloads, Papers with Code entries and deployed-demo URLs move you above candidates with equal experience.
• Every experience bullet needs a number: "deployed LLM serving 8M daily requests at p99 220ms" beats "worked on LLM deployment" by an order of magnitude in both ATS and LLM screening.
• Single-column layout only. 2026 parsers still butcher two-column templates and sidebars. Both the classic ATS and the LLM ranking layer need clean text.

What makes an AI engineer CV different in 2026

The AI engineer CV 2026 candidates now write is a technical CV that must clear a two-layer screening stack: a classic ATS parsing for keywords and structure, and an LLM ranking layer that reads for meaning and specificity. The differences from a general software engineering CV are concrete — the skills section is longer and grouped by family, the projects section carries more weight than for other roles, and the recruiter is now often another AI system trained to spot the difference between real production deployment and a course-project portfolio. Employment demand for the role backs the pressure: the US Bureau of Labor Statistics projects computer-and-information-research (which covers AI/ML) growing at multiple times the average for all occupations through 2033.

Three shifts define the 2026 hiring bar for the AI engineer CV 2026 recruiters actually shortlist:

1. Generative AI is now table stakes. LLM, RAG and prompt-engineering experience is expected on every AI or ML engineer CV, even for roles where it is not the primary focus. Missing it reads as career-lag.
2. Production deployment beats research. Frontier labs (OpenAI, Anthropic, Google DeepMind) and scaled enterprise ML teams both screen for production evidence first — deployed models, MLOps pipelines, on-call rotation. Kaggle rankings and course projects carry less weight than in 2022.
3. Portfolio is scanned before experience. GitHub, Hugging Face model page, live demo URL and a Papers with Code entry each carry more signal than a bullet in the experience section. Recruiters click them before deciding whether to keep reading.

The 2026 AI engineer CV is judged by whether the reader — human or LLM — can answer one question in 15 seconds: has this person shipped a real AI system that a real user has touched?

The 2026 AI engineer tech stack recruiters actually scan for

Group your skills into these six families. Every strong AI engineer CV 2026 recruiters shortlist follows this grouping. This grouping mirrors how recruiters and LLM screening tools now categorise AI engineer skills:

Two guardrails on the skills list. First, only include what you can defend in an interview — the LLM screening layer specifically flags CVs where the skills claim is not supported by the experience bullets. Second, list Agentic AI and multimodal only if you have shipped or seriously experimented with them. Faking currency is easy to spot on a technical interview.

AI engineer CV template: section-by-section

The AI engineer CV 2026 layout that both filters read cleanly runs in this order, tuned for both the classic ATS and the LLM ranking layer:

1. Contact block (top-left, 4 lines). Name, city + remote status, email, phone, LinkedIn URL, GitHub URL, portfolio URL. The four URLs are read first by both filters. Put them on one line each, not merged.
2. Professional summary (2-3 sentences, one flagship metric). Format: seniority + primary stack + one flagship achievement. "Senior AI engineer with 6 years of production LLM deployment, PyTorch and MLOps on AWS. Shipped a RAG system serving 8M daily requests at p99 220ms for a Fortune-500 bank."
3. Skills, grouped by family. Six lines, one per family (see the section above). Not an alphabetical wall.
4. Experience, most recent first. 3-7 bullets per role, every bullet tool-first and quantified. Details in the bullets section below.
5. Projects (as a full section, not a footnote). 2-3 projects with a live link, stars/users/downloads number, and one line on the technical challenge solved.
6. Education. One or two lines. Include relevant coursework only if you are early-career; drop it if you have 5+ years of experience.

Single column, no sidebars, no tables in the body. Both the classic ATS and the LLM parser need a clean top-to-bottom text flow. If you need the general CV mechanics before customising for AI, our how to write a CV guide covers the fundamentals.

The skills section: grouping that beats alphabetical

Every AI engineer CV that scores well in 2026 has skills grouped by family — never a single alphabetical list. Here is the exact grouping that both the classic ATS and the LLM ranking layer parse cleanly:

Languages: Python, Go, SQL
ML/DL: PyTorch, TensorFlow, JAX, scikit-learn, XGBoost, HuggingFace Transformers
LLM stack: RAG, LangChain, LangGraph, vLLM, Ragas evaluation, Anthropic SDK, OpenAI SDK
MLOps: Kubernetes, Docker, MLflow, Airflow, Terraform, DVC, GitHub Actions
Cloud: AWS (SageMaker, Bedrock, EKS), GCP (Vertex AI), Azure (AI Foundry)
Data: Spark, dbt, Snowflake, BigQuery, Feast

Two rules that consistently move CVs up the shortlist. First, list the LLM stack and MLOps families before the older ML/DL family — this is where 2026 hiring pressure sits. Second, mark the two or three items you would rate yourself 9/10 on with a bold or an asterisk convention — recruiters will ask about those specifically, and the LLM ranking layer weighs signalled depth higher than a flat list.

The portfolio section: GitHub, Hugging Face, papers, demos

The portfolio section is what separates a mid-pack AI engineer CV from a top-of-shortlist one in 2026. Frontier-lab recruiters click every link before reading a single experience bullet. Include 2-4 items, each with a URL, a metric, and a one-line description of the technical challenge. The two portfolio hubs recruiters check most often in 2026 are Hugging Face model pages and Papers with Code:

• GitHub repository. The strongest single signal. Aim for one repository with 100+ stars, meaningful commits over 6+ months, and a real README with benchmarks or usage examples. If you cannot list stars, list contribution graphs.
• Hugging Face model or dataset page. A fine-tuned model with monthly download numbers is a stronger signal than a repository. Include the model name, base model, task, and downloads.
• Deployed live demo. A URL that works, ideally with user counts or a public availability metric. Vercel, Modal, Hugging Face Spaces are common hosts. A broken link is worse than no link at all — check monthly.
• Papers with Code entry, arXiv paper, or conference workshop paper. For research-adjacent roles this signals depth. For pure engineering roles it is a plus, not a must.

The portfolio section also gives the LLM ranking layer something concrete to quote back to the recruiter — the same mechanism our AI resume screening 2026 guide describes for how the LLM generates its two-sentence rationale.

Experience bullets: quantified AI achievements with 8 real examples

Every experience bullet on an AI engineer CV needs a number. The LLM ranking layer weighs quantified bullets significantly higher and generates a specific rationale that lands you on the shortlist. Here are 8 real bullet patterns you can adapt — each shows the shape recruiters actually score well:

1. LLM production deployment: "Deployed a Llama-3-70B RAG system serving 8M daily requests at p99 220ms latency, cutting customer-service handling time 34% and saving $2.1M annualised at a Fortune-500 bank."
2. Model fine-tuning: "Fine-tuned a Mistral-7B model with QLoRA on 1.2M internal support tickets, lifting first-response accuracy from 71% to 89% against a held-out eval set."
3. MLOps pipeline: "Rebuilt the model-training pipeline on Kubeflow + MLflow + Terraform, cutting weekly training-to-production cycle from 12 hours to 45 minutes across 6 ML teams."
4. Evaluation framework: "Designed and shipped a Ragas-based LLM evaluation harness with 14 metric families, adopted by 3 product teams and blocking 12 regression releases in the first quarter."
5. Cost optimisation: "Introduced vLLM + speculative decoding on a customer-facing GenAI product, reducing inference cost per query 72% without measurable quality loss."
6. Agentic system: "Built an agentic support-triage system with LangGraph orchestrating 4 tools (search, ticket-DB, CRM, escalation), resolving 38% of tier-1 tickets end-to-end without human handover."
7. Multimodal: "Shipped a multimodal claims-processing model (vision + text) on Vertex AI, replacing a 6-person manual review team and processing 140K documents/month with 96% agreement on gold-standard sample."
8. Team leadership: "Led a 4-engineer AI platform team, hired 2, ran the on-call rotation for our LLM gateway serving 12M requests/day, and cut mean time to recovery from 42 minutes to 8."

Structural notes on bullets: lead with the tool or system, embed the metric mid-bullet, and end with the business outcome. Not "responsible for LLM deployment" — "Deployed Llama-3-70B RAG serving 8M requests, saved $2.1M". For behavioural interview stories to accompany these bullets, see our STAR method interview examples 2026 guide.

What the LLM screening layer looks for on your CV

Half of AI engineer CVs in 2026 are pre-screened by an LLM before a human reads them. The LLM has been prompted by the recruiter to score the CV 1-10 against the job description and write a two-sentence rationale. Understanding what it weighs is more useful than any template:

The LLM is not counting keywords — it is checking whether the tools you list are supported by concrete achievements in the experience section. A skills list that mentions Kubernetes with zero deployment bullets scores lower than a shorter list backed by evidence.

Four signals the LLM screening layer consistently weighs high:

• Skills-experience coherence. Every headline skill needs at least one experience bullet that demonstrates it. Listing 20 skills without evidence is worse than listing 8 with proof.
• Quantified outcomes over verbs. Bullets with numbers get quoted verbatim in the LLM's rationale. Bullets without numbers get compressed to "worked on X".
• Production-signal keywords. "Deployed", "serving X requests/day", "on-call", "SLO", "p99 latency", "cost per query" — all specifically weighted because they signal real ownership.
• Portfolio URLs that resolve. The LLM may not click, but the scoring prompt often instructs it to note whether portfolio links look real (specific model or repo names) vs placeholder.

Full mechanics of how the two-layer stack scores CVs, and the exact prompt shapes recruiter tools use, are in our AI resume screening 2026 guide.

Six mistakes that get AI engineer CVs rejected

1. Two-column template with a sidebar. The 2026 classic ATS still butchers these — your beautiful CV becomes garbled text in the parser. Single column, top-to-bottom.
2. Skills list without evidence. Listing 20 tools with zero corresponding experience bullets is the most common LLM-screening downgrade. Cut skills you cannot back up.
3. No portfolio URLs. The single biggest missable signal in 2026. Even one Hugging Face model or a GitHub repository with real commits is a differentiator.
4. Missing GenAI/LLM experience. If your last AI project pre-dates 2023, the CV reads as career-lag. Add a recent side project even if your day job is classical ML.
5. Unquantified bullets. "Improved model performance" is invisible to the LLM ranking layer. "Cut p99 latency 62% via speculative decoding" is quotable.
6. Same CV for research and production roles. Frontier-lab research roles weigh papers and reproducibility; enterprise ML roles weigh deployed systems and MLOps. A CV that leads with the wrong signal for the role gets shortlisted below candidates who tailored.

A full AI engineer CV example (700 words, real signals)

Here is a full worked AI engineer CV example — fictional but every signal is realistic and would score well against a 2026 senior AI engineer job at a scaled enterprise or an AI-first startup:

Maya Chen
Berlin (open to remote in EU/UK) · maya.chen@example.com · +49 30 12345678
LinkedIn: /in/maya-chen-ai · GitHub: github.com/mchen-ai · Portfolio: mayachen.dev

Summary. Senior AI engineer with 7 years of production ML and 2 years of LLM deployment on AWS and GCP. Shipped a RAG system serving 8M daily requests at p99 220ms for a Fortune-500 bank; led the migration of a 6-model training pipeline to Kubeflow, cutting training-to-prod cycle from 12 hours to 45 minutes.

Skills.
Languages: Python, Go, SQL, Rust (basics)
ML/DL: PyTorch, TensorFlow, HuggingFace Transformers, XGBoost, scikit-learn
LLM stack: RAG, LangGraph, vLLM, Ragas eval, Anthropic SDK, OpenAI SDK, QLoRA fine-tuning
MLOps: Kubernetes, Docker, MLflow, Airflow, Kubeflow, Terraform, GitHub Actions
Cloud: AWS (SageMaker, Bedrock, EKS), GCP (Vertex AI), Azure (AI Foundry)
Data: Spark, dbt, Snowflake, BigQuery, Feast

Experience.

Senior AI Engineer · Nexbank · Sep 2024 - present
• Deployed a Llama-3-70B RAG system serving 8M daily requests at p99 220ms, cutting customer-service handling time 34% and saving $2.1M annualised.
• Introduced vLLM + speculative decoding on the customer-facing GenAI product, reducing inference cost per query 72% without measurable quality loss.
• Designed and shipped a Ragas-based LLM evaluation harness with 14 metric families, adopted by 3 product teams and blocking 12 regression releases in Q1.
• Ran the on-call rotation for the LLM gateway (12M requests/day), cutting mean time to recovery from 42 minutes to 8.

Machine Learning Engineer · Datacraft · Jan 2022 - Aug 2024
• Fine-tuned a Mistral-7B model with QLoRA on 1.2M internal support tickets, lifting first-response accuracy from 71% to 89% on a held-out eval set.
• Rebuilt the model-training pipeline on Kubeflow + MLflow + Terraform, cutting the weekly training-to-production cycle from 12 hours to 45 minutes across 6 ML teams.
• Shipped a multimodal (vision + text) claims-processing model on Vertex AI, replacing a 6-person manual review team and processing 140K documents/month with 96% agreement on gold-standard sample.

Machine Learning Engineer · Startup (Series A) · Jun 2019 - Dec 2021
• Built and deployed the first company-wide recommendation model (LightGBM + implicit feedback), lifting session-level engagement 22% and driving $6M incremental annual revenue.
• Established the ML platform (feature store on Feast, model registry on MLflow, monitoring via Grafana), adopted by 4 subsequent hires.

Projects.
• legal-rag (github.com/mchen-ai/legal-rag) — open-source EU legal-document RAG library, 1.4k stars, 6 external contributors.
• mchen-ai/mistral-7b-finance-qa (Hugging Face) — fine-tuned Mistral-7B for finance Q&A, 8k monthly downloads.
• mayachen.dev/eu-directive-explainer — live LangGraph agent explaining EU regulation, 12k monthly users.

Education. M.Sc. Machine Learning · TU Munich · 2019

Next steps

Once your AI engineer CV 2026 layout is structured to this shape, run it through the same two-layer stack that hiring companies use: parse it in a classic ATS check to confirm the skills and experience come through cleanly, then read it back and ask whether the LLM ranking layer could write a specific two-sentence recommendation. Our free CV builder is tuned for exactly this shape — grouped skills, quantified bullets, portfolio as a section — and pairs with the AI resume screening 2026 guide to explain what the LLM layer scans for. For the interview stage that follows, work through the STAR method interview examples 2026 guide and the video interview tips guide — AI-engineer loops now often start with an async video round before the technical deep-dive.

Frequently Asked Questions

What should be at the top of an AI engineer CV in 2026?

Contact details on the first line, followed immediately by LinkedIn, GitHub and portfolio URLs. Then a 2-3 sentence professional summary that includes seniority, primary stack and one flagship metric — for example "Senior AI engineer with 6 years of LLM production deployment, PyTorch and MLOps on AWS. Shipped a RAG system serving 8M daily requests at p99 220ms." Recruiters and the LLM screening layer both read this block first.

How long should an AI engineer CV be in 2026?

One page for candidates with under 5 years of experience, two pages for senior candidates with a substantial project or paper portfolio. For frontier-lab applications (OpenAI, Anthropic, Google DeepMind) two pages is often the norm because the projects and publications section carries weight. Never three pages — the LLM ranking layer down-weighs over-long CVs.

Which skills matter most on an AI engineer CV in 2026?

The LLM stack (Transformers, RAG, LangChain/LangGraph, vLLM, evaluation frameworks like Ragas), Python, PyTorch, MLOps tools (Kubernetes, Docker, MLflow, Airflow, Terraform), and at least one cloud AI platform (AWS SageMaker/Bedrock, GCP Vertex AI, Azure AI Foundry). Agentic AI (CrewAI, Claude Agent SDK, LangGraph) and multimodal signal currency and are the fastest-growing 2026 specialisations.

Do I need a GitHub link on my AI engineer CV?

Effectively yes in 2026. Frontier-lab and scaled-startup recruiters click GitHub before reading experience bullets. Aim for at least one repository with 100+ stars, meaningful commits over 6+ months, and a real README with benchmarks. If your best work is closed-source, list a Hugging Face model page or a deployed-demo URL instead.

How should I show LLM and generative AI experience if my day job is classical ML?

Add it under a projects section with a live URL and a metric. A fine-tuned Hugging Face model with monthly downloads or a deployed LangGraph agent with user counts both signal current LLM experience without requiring your day-job title to match. Missing any GenAI evidence in 2026 reads as career-lag.

What is the difference between an AI engineer CV and a machine learning engineer CV?

The lines are blurred in 2026. ML engineer typically leans more on classical ML, MLOps and data engineering; AI engineer leans more on LLM/GenAI, agentic systems and inference optimisation. In practice the same CV can target both if the skills section covers both families and the projects section includes at least one deployed LLM system alongside classical ML work.

Should I include Kaggle competitions on an AI engineer CV?

Only if you have a top 5% finish and you are early in your career, or if the role is research-adjacent. For most 2026 production AI engineering roles, Kaggle rankings carry less signal than one deployed LLM demo or a Hugging Face model with real download numbers. Replace Kaggle with production evidence where possible.

Do AI engineer CVs get screened by AI in 2026?

Yes, at almost every large employer. A classic ATS parses the structure and matches keywords, then an LLM ranking layer scores the CV 1-10 against the job description and generates a short rationale a recruiter reads. The full mechanism is covered in our AI resume screening 2026 guide — the practical takeaway for your CV is: single column, grouped skills, quantified bullets, portfolio URLs.

What layout parses best for an AI engineer CV in 2026?

Single column, black text on white, standard section headings (Summary, Skills, Experience, Projects, Education), consistent date format on the right, text-based PDF exported from a real word processor. Avoid two-column templates with sidebars, tables inside the body, custom fonts and image-based section headers — the 2026 parser still misreads all of them.

How do I tailor my AI engineer CV for a specific company?

Rewrite three things per application: the summary line to lead with the flagship metric closest to the target role, the top two skills-family orderings to put the target company's stack first, and the top-of-experience bullets to match the language of the job description. Keep the projects section stable — that is your evergreen signal.