AI Engineer Resume Example & Template (2026)

PwC’s 2025 AI Jobs Barometer found a 56% wage premium for roles that require AI skills versus equivalent roles without them, up from 25% just one year earlier. Veritone’s Q1 2025 analysis counted 35,445 open AI positions across the US, a 25.2% jump from the same quarter in 2024, with a median posted salary of $156,998. The BLS projects software developer employment overall to grow 15% from 2024 to 2034, and AI-adjacent roles are outpacing that projection considerably.

The demand is real, but so is the noise. An AI Engineer posting at a Series B startup or a Fortune 500 innovation lab attracts hundreds of applications, and most are screened by the same ATS tools the candidates are now building for other companies. Passing that filter requires a resume that uses the right vocabulary — RAG, LLM fine-tuning, vector databases, evaluation pipelines — and pairs each skill with a quantified outcome. This page gives you a complete sample resume, a section-by-section explanation of the decisions behind it, ATS keyword guidance, and the five mistakes that eliminate otherwise strong candidates.

Full Sample Resume

Jordan Kim Seattle, WA · jordan.kim@email.com · linkedin.com/in/jordankim-ai · github.com/jordankim-ai

Summary

AI Engineer with 4 years building and shipping production LLM-powered applications for enterprise SaaS. Reduced customer support ticket volume 34% at CloudOps by designing a RAG-based internal knowledge assistant serving 1,200 daily active users. Experienced across the full GenAI stack: prompt engineering, OpenAI and Anthropic API integration, vector database design, fine-tuning workflows, and LLM observability. Seeking a senior AI Engineer role where application quality and inference cost are both first-class concerns.

Experience

Senior AI Engineer — CloudOps Platform, Seattle, WA March 2023 – Present

• Designed and shipped a Retrieval-Augmented Generation (RAG) knowledge assistant using LangChain, OpenAI GPT-4o, and Pinecone; system ingests 14,000+ internal documents and serves 1,200 daily active users, reducing support ticket volume by 34% in the first 90 days post-launch.
• Cut LLM inference costs by 41% over two quarters by implementing semantic caching with Redis, batching low-latency requests, and migrating a subset of classification tasks from GPT-4 to a fine-tuned GPT-3.5-turbo model without measurable accuracy regression.
• Built an automated LLM evaluation pipeline using Python and the Ragas framework; pipeline runs 600 test cases on every pull request, catching prompt regressions before they reach production and reducing post-deploy rollbacks by 60%.
• Mentored two mid-level engineers on RAG architecture patterns and prompt engineering best practices; both shipped independent AI features within three months.

AI Engineer — Meridian Analytics, San Francisco, CA June 2021 – February 2023

• Developed a document intelligence pipeline using Azure OpenAI and custom chunking strategies to extract structured data from 50,000+ unstructured contracts; reduced manual review time per document from 22 minutes to under 3 minutes.
• Integrated multi-agent workflows using LangChain Agents and custom tool definitions to automate competitive-intelligence research; system replaced 15 hours/week of analyst effort and delivered structured summaries to the executive team daily.
• Deployed fine-tuned embedding models on AWS SageMaker for a semantic search feature; retrieval precision@5 improved from 61% to 84% compared to keyword-based BM25 search.

Machine Learning Engineer — Startup Incubator, Remote August 2020 – May 2021

• Built a text classification service (FastAPI, Hugging Face Transformers) that routed 18,000 monthly inbound inquiries to the correct department with 91% accuracy, replacing a keyword-routing system that ran at 67%.
• Containerized all ML services with Docker and established a GitHub Actions CI/CD pipeline that reduced deployment cycle time from two days to under two hours.

Skills

AI / LLM: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, Fine-Tuning (LoRA, PEFT, OpenAI fine-tune API), LangChain, LlamaIndex, LLM Evaluation (Ragas, LangSmith), Semantic Caching, Multi-Agent Systems

Languages & Frameworks: Python, FastAPI, REST APIs, SQL

Vector & Data: Pinecone, Weaviate, Chroma, PostgreSQL + pgvector, Redis

Cloud & MLOps: AWS SageMaker, Azure OpenAI, GCP Vertex AI, Docker, Kubernetes, GitHub Actions, MLflow

APIs & Models: OpenAI API, Anthropic Claude API, Hugging Face Hub, Cohere

Education

B.S. Computer Science — University of Washington, Seattle, WA — 2020 Relevant coursework: Machine Learning, Natural Language Processing, Distributed Systems, Algorithms

Why This Resume Works: Section by Section

Summary

The summary does three things in four sentences: it names the role clearly (AI Engineer, not ML Engineer), it leads with a single strong proof point (34% ticket reduction), and it signals the technical depth that separates candidates who integrate LLMs from those who merely use ChatGPT. The phrase “inference cost” in the last sentence matters specifically because cost management is a top hiring pain point in 2026 — most companies deploying LLMs at scale have already received an unexpectedly large API bill.

Avoid a summary that lists adjectives (“passionate,” “collaborative,” “results-driven”) without evidence. Hiring managers skip to the bullets. The summary is your one chance to anchor the narrative before they do.

Experience Bullets

Every bullet follows the same architecture: action verb → specific technology or system → measurable result. Notice what is not in these bullets: vague scope language (“worked on AI initiatives”), feature descriptions without outcome (“built a chatbot”), or inflated titles without substance (“led AI transformation”).

The metrics are grounded in the kind of data an engineer would actually have: ticket volume (available in Zendesk or Jira), cost savings (visible in the API billing dashboard), precision@5 (a standard IR metric any engineer running retrieval evals would track), and CI/CD cycle time (available in GitHub Actions logs). These are not invented statistics — they reflect the measurements that teams actually instrument when they care about system quality.

Quantifying three or four bullets per role is sufficient. Forcing numbers onto every line often produces weak metrics that undermine credibility.

Skills Section

The Skills section is where ATS keyword matching happens most reliably. The structure here separates skills into logical clusters (AI/LLM, Languages, Vector/Data, Cloud/MLOps, APIs) rather than dumping a flat list. This helps human reviewers locate relevant skills quickly while still exposing all keywords to the parser.

Two formatting choices are intentional. First, both acronyms and full terms appear together for the most important concepts (“Large Language Models (LLMs)”, “Retrieval-Augmented Generation (RAG)”). Second, competing tools in the same category are listed together (“Pinecone, Weaviate, Chroma”) because job descriptions vary in which vector database they reference and covering multiple options improves match rate without looking padded.

Education

For a 4-year AI Engineer, education is brief by design. The relevant coursework callout signals NLP and ML foundations to hiring managers who want to verify that the candidate understands what the LLMs they integrate are doing internally. If you have a graduate degree in CS, ML, or a related field, lead with that. If you have no formal CS degree, supplement with a certification line (AWS Certified Machine Learning Specialty, DeepLearning.AI specialization) immediately below education.

ATS Keyword Guidance

AI Engineer job descriptions in 2026 cluster around a few consistent keyword categories. The following terms appear in the highest proportion of active postings and carry the most weight with ATS filters.

LLM integration and application: Retrieval-Augmented Generation (RAG), LangChain, LlamaIndex, LLM fine-tuning, prompt engineering, system prompt design, multi-agent systems, function calling, tool use, OpenAI API, Anthropic API, Hugging Face

Vector and retrieval infrastructure: vector database, Pinecone, Weaviate, Chroma, pgvector, embedding models, semantic search, chunking strategy, hybrid search

Evaluation and observability: LLM evaluation, Ragas, LangSmith, hallucination detection, groundedness, faithfulness, LLM tracing, prompt regression testing

MLOps and deployment: MLflow, Docker, Kubernetes, CI/CD for ML, model monitoring, inference optimization, semantic caching, token cost management

Cloud AI services: AWS SageMaker, Azure OpenAI, GCP Vertex AI, AWS Bedrock

Two tactics make a meaningful difference with modern ATS systems:

First, mirror the job description’s exact phrasing. If a posting says “Retrieval-Augmented Generation” in one place and “RAG pipeline” in another, include both in your resume. ATS tools built on keyword matching treat these as distinct strings until a synonym dictionary entry covers them, and many do not have those entries for relatively new technical terms.

Second, include keywords in context, not just in the Skills section. A bullet that says “designed a RAG pipeline using LangChain and Pinecone to serve 1,200 daily users” will score higher than a Skills section entry alone because the keyword appears in a semantic context that suggests real usage rather than a keyword list added to pass a filter.

5 Common AI Engineer Resume Mistakes

1. Writing an ML Engineer resume with an AI Engineer title

These are different roles in 2026, and hiring managers know it. ML Engineers build and train models — they run experiments, tune hyperparameters, and optimize training loops. AI Engineers integrate and deploy models — they build applications on top of APIs, design RAG architectures, and manage inference pipelines. If your bullets describe training custom neural nets from scratch but the role is an AI Engineer position focused on LLM application development, the mismatch will cost you the interview. Align your framing to the role. If you have both profiles, maintain two versions of your resume.

2. Listing tools without outcomes

“Experience with LangChain, Pinecone, and OpenAI API” tells a recruiter you have heard of these tools. “Built a RAG assistant using LangChain and Pinecone that reduced support ticket volume 34%” tells them you can deliver working software with them. AI engineering is still new enough that many candidates have credentials and certifications without production deployments. A resume that shows shipped work — even from a side project with real users — stands out sharply against a list of tools.

3. Ignoring cost and evaluation metrics

In 2025 and 2026, every AI team with a real budget tracks two things obsessively: how accurate the system is (evaluation metrics like precision, recall, faithfulness, hallucination rate) and how much inference is costing per month. Resumes that show candidates who instrument these concerns — “reduced inference cost 41%,” “evaluation pipeline catches prompt regressions before deploy” — signal seniority and commercial awareness that generic AI skill lists do not.

4. Using unformatted or graphically complex layouts

This mistake affects all technical resumes but hits AI engineers with an extra layer of irony: the same ATS tools the candidate may be building for other industries will strip tables, text boxes, and multi-column layouts before a human reads the document. A two-column design that looks polished in a PDF viewer often arrives at the recruiter as a garbled single column with skills and job titles merged together. Use a single-column layout with standard section headings (Summary, Experience, Skills, Education). Save the design creativity for your portfolio site.

5. Omitting the operationalization layer

A common gap in mid-level AI Engineer resumes is everything that happens after a model or LLM integration is first working: monitoring for drift or quality degradation, evaluation pipelines that run on CI, cost tracking, latency SLOs, rollback procedures. These concerns are invisible in a resume that lists only the initial build. For any production AI system, add at least one bullet describing how you measured ongoing quality or controlled ongoing cost — it is the fastest signal that you have shipped to real users and maintained the system afterward, not just built a demo.