AI Engineer Resume Examples & Writing Guide

A one-page resume for an AI engineer with five years of experience, the last three building LLM systems in production. The name and companies are fictional. Read the bullets for the pattern: what was built, for how many users, how it was measured, and what changed in the metric.

AI engineer postings get hundreds of applicants, many of them people who finished a course last month and call themselves AI engineers. The hiring manager (usually a senior engineer or head of AI) is sorting for people who have shipped. Four things decide it.

• Production, not notebooks. Did real users hit your system? How many, how often? "Built a chatbot with LangChain" is a tutorial. "RAG assistant used by 8,000 clinicians, 120,000 queries a month" is a job.
• Evaluation. The single biggest separator in 2026. Did you build an eval set, how big, who labeled it, what metric, and how did the number move? If your resume has no eval numbers, the manager assumes you shipped on vibes.
• Cost and latency. Anyone can call a large model. Engineers who cut cost per query, p95 latency or GPU spend, and can say by how much, are rare and get interviews.
• Depth in one layer plus breadth across the stack. Retrieval, fine-tuning, serving, or evals as your strong point, with working knowledge of the rest, including the boring parts: data pipelines, deployment, monitoring, security review.

“I look for two numbers on every AI resume: an eval metric that moved and a cost or latency number that dropped. If both are there, the person has run something in production. If neither is, it is a portfolio of demos, and we have plenty of those.”

For new graduates and career changers, the projects section replaces work experience. For everyone else it is where you show the depth your job did not cover. Three rules: link the repo, treat each project like a job bullet (problem, stack, metric), and make sure the README of anything you link is good enough that a stranger can run it in ten minutes.

Three or four lines: years and the kind of AI work, the strongest system you shipped with its scale, two metrics, the stack. Entry-level candidates use an objective that names the projects and the role.

The pattern: what you built, who used it and at what scale, how you measured it, what changed. Name the technique when it explains the result.

Group skills by layer so the reader can see your shape, and keep it to what you could whiteboard. Recruiters screen on tool names; engineers interview on whether you actually used them.

• Languages: Python first with years, then SQL; add Go, TypeScript, Rust or C++ only if you have written production code in them.
• Modeling: PyTorch (or JAX), Hugging Face Transformers, PEFT and LoRA/QLoRA, scikit-learn, gradient-boosted trees; distributed training (DeepSpeed, FSDP) if you have done it.
• LLM application layer: model provider APIs (OpenAI, Anthropic, Bedrock, Vertex AI), tool calling and structured outputs, agent frameworks, LangChain or LlamaIndex, prompt versioning.
• Retrieval: vector stores (pgvector, Pinecone, Weaviate, OpenSearch, Milvus), hybrid search, chunking, embeddings, rerankers, evaluation of retrieval separately from generation.
• Evaluation and monitoring: building labeled eval sets, LLM-as-judge with human calibration, Ragas, promptfoo, A/B testing, tracing (LangSmith, Arize Phoenix, Langfuse), drift monitoring.
• Serving and infrastructure: vLLM or TGI, FastAPI, Docker, Kubernetes, one major cloud named specifically (SageMaker, Bedrock, Vertex AI, Azure AI Foundry), GPU cost management, caching, streaming.
• Data and MLOps: Spark, Airflow or Dagster, dbt, MLflow or Weights & Biases, feature stores, CI/CD.
• Safety and compliance: PII/PHI redaction, prompt-injection defenses, guardrails, model cards, HIPAA, SOC 2 or GDPR data handling where relevant.

Entry-level AI engineer postings are rare and crowded. Two paths work: a computer science or related degree plus two or three projects with evaluation numbers, or a software or data job you can rewrite around the ML and LLM work you did in it. Either way, the page is built around evidence, not coursework.

1. 1Objective (two lines): the role, your degree or current job, and your two best project results with metrics.
2. 2Projects section directly under the objective: two or three entries, each with a GitHub link, the problem, the stack, the eval set and the metric. Write them like the job bullets above.
3. 3Experience: internships, research assistantships, teaching assistant work for ML courses, and any job where you automated or modeled something. Give users, data size and results.
4. 4Education: degree, school, year, GPA if above 3.5, and a short list of relevant courses (machine learning, NLP, distributed systems, linear algebra). A thesis or capstone with a real dataset counts as a project.
5. 5Skills: only what appears in your projects. If Kubernetes is in your skills list, a recruiter expects to see a deployment in a repo.
6. 6Apply broadly to titles: ML engineer, applied scientist, AI/ML software engineer, data scientist (LLM), and generalist backend roles at companies building AI products. Many people get their first AI title one job later.

Most AI engineer postings ask for a bachelor's in computer science or a related field, and many say a master's or PhD is preferred for research-heavy roles. No certification is required anywhere in the field, and none substitutes for a shipped system. A few are recognized and worth one line if you hold them.

• AWS Certified Machine Learning Engineer – Associate, and AWS Certified AI Practitioner (foundational). Useful when the company runs on AWS.
• Google Cloud Professional Machine Learning Engineer, for Vertex AI shops.
• Microsoft Certified: Azure AI Engineer Associate (exam AI-102), common at enterprises on Azure OpenAI.
• NVIDIA Deep Learning Institute certificates for training and inference optimization.
• Course certificates (DeepLearning.AI, fast.ai, university MOOCs): list at most one or two under education for an entry-level resume, none once you have work experience. Employers read them as learning, not proof.
• List the degree, school and year. Add the thesis title only if it relates to the job. Do not list high school.

• One page up to about ten years of experience. Two pages only for staff-level engineers or people with publications to list. Cut old jobs to two bullets before you go to page two.
• Order: header with GitHub and LinkedIn links, summary, experience (or projects first for entry-level candidates), projects, skills, education. Publications after education if you have them.
• Single column, plain fonts, no photo, no skill bars or percentage circles. LaTeX resumes are common and fine in this field; export to a text-based PDF and check that the text copies out cleanly.
• Make the links real hyperlinks and also readable as text (github.com/you), since some applicant systems strip links.
• Keywords to mirror from the posting exactly: "LLM", "RAG" and "retrieval-augmented generation" (use both once), "fine-tuning", "PyTorch", "evaluation", "vector database", "agents", "MLOps", the cloud name, and "production". Recruiters filter on these before an engineer reads anything.
• LinkedIn: use the same title and the same top metrics as the resume. Recruiters find AI engineers by searching LinkedIn for "RAG", "LLM" and a cloud name, then compare the profile with the resume you sent. Mismatches cost interviews.
• Outside the US the document is an AI engineer CV and may run to two pages; the content advice is the same. Never include a photo or date of birth for US applications.

Run the posting and your resume through the cvplex ATS checker. It flags missing stack keywords, bullets without a metric, and links that did not survive the PDF export.