Resume Example

AI EngineerResume Example

Use this AI engineer resume example to show how to present LLM applications, RAG pipelines, evaluation, and production deployment work in a clear, ATS-friendly format.

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AISHA KHAN

AI Engineer

aisha.khan@email.com · San Francisco, CA · linkedin.com/in/aishakhan · github.com/aishakhan

Summary

AI engineer with 4+ years of experience building LLM features, including RAG pipelines and agents, with Python, FastAPI, LangChain, and OpenAI and Anthropic APIs.

Skills

Python · FastAPI · LLMs · RAG · embeddings · pgvector · Pinecone · LangChain · evals · guardrails · prompt engineering

Experience

AI Engineer

Northstar AI Products

Built RAG pipelines with embeddings and pgvector so an LLM assistant answered grounded, cited questions.

Created an eval pipeline and guardrails that caught accuracy regressions before each release.

Cut LLM token cost by ~35% through chunk tuning, retrieval caching, and model routing.

What a AI Engineer Resume Should Prove

A strong AI engineer resume should show more than calling an LLM API. It should prove that you can build RAG and agent pipelines, design embeddings and retrieval, run evals and guardrails, and ship reliable LLM features to production with measurable quality and cost control.

LLM application depth

Show the RAG pipelines, agents, or LLM features you built with model APIs, LangChain, or LlamaIndex to solve a real product problem.

Retrieval and evaluation

Highlight embeddings, vector databases, prompt engineering, and eval or guardrail pipelines that made LLM output accurate and safe.

Production and cost impact

Use evidence around accuracy, latency, hallucination reduction, token cost, or user adoption that shows your AI features actually worked.

AI Engineer Resume Example Sections

Below is a practical AI engineer resume example you can adapt to your own experience. Use the structure and level of detail as a guide, then tailor the wording to the LLM applications, RAG pipelines, and evaluation work you have actually shipped.

1. Summary Example

AI engineer with 4+ years of experience building LLM-powered features, including RAG pipelines and agents, using Python, FastAPI, LangChain, and OpenAI and Anthropic APIs. Strong focus on embeddings and vector search with pgvector and Pinecone, prompt engineering, eval and guardrail pipelines, and shipping reliable, cost-aware AI to production.

Tip: Keep your summary focused. Mention the LLM features you build, your retrieval and eval stack, and how you ship reliable AI rather than listing every model or library you have tried.

2. Skills Example

Languages and APIs: Python, FastAPI, OpenAI API, Anthropic API

LLM frameworks: LangChain, LlamaIndex, prompt engineering, function calling

Retrieval: RAG, embeddings, semantic search, chunking

Vector databases: pgvector, Pinecone, Weaviate, hybrid search

Quality and safety: evals, guardrails, hallucination reduction, LLM observability

Advanced: agents, fine-tuning, RAG evaluation, token cost optimization

Tip: An AI engineer resume is strongest when the skills section matches the systems you describe elsewhere. List RAG, vector databases, evals, or agents only when your bullets or projects prove them.

3. Experience Bullet Examples

Built RAG pipelines with embeddings and a vector database so an LLM assistant could answer questions grounded in company documents instead of hallucinating.
Designed prompt templates, function calling, and retrieval steps with LangChain and OpenAI and Anthropic APIs to power production LLM features.
Built an eval pipeline with curated test sets and guardrails that measured accuracy and caught regressions before each prompt or model change shipped.
Reduced token cost and latency by tuning chunking, caching retrievals, and routing simpler requests to smaller models.
Added LLM observability with traced requests, output logging, and quality metrics to monitor and improve features in production.

Tip: Strong AI engineer bullets usually mention the LLM feature, the retrieval or eval technique, and the outcome for accuracy, hallucination rate, cost, or user adoption.

4. Project Example

Documentation RAG Assistant

Built a retrieval-augmented assistant that answers questions over a documentation set with citations. The project demonstrates embeddings, vector search, prompt design, and evaluation that maps directly to AI engineering roles.

Chunked and embedded documents and stored vectors in pgvector for semantic retrieval.
Built a FastAPI service with a LangChain RAG chain that returned answers with source citations.
Created an eval set and measured answer accuracy and citation correctness across prompt versions.
Added guardrails to refuse out-of-scope questions and reduce hallucinated answers.

Tip: AI projects are strongest when they show the retrieval design, the prompts, the evaluation method, and how you kept output accurate and grounded.

AI Engineer Skills to Include

The best AI engineer skills depend on the role, but most AI engineer resumes should include a mix of Python and APIs, LLM frameworks, retrieval and embeddings, vector databases, evaluation and guardrails, and production or observability skills.

Core AI engineering skills: Python, LLMs, prompt engineering, RAG, embeddings, FastAPI

Frameworks and APIs: LangChain, LlamaIndex, OpenAI API, Anthropic API, function calling, streaming

Retrieval and storage: pgvector, Pinecone, Weaviate, semantic search, chunking, hybrid search

Quality and operations: evals, guardrails, LLM observability, fine-tuning, token cost optimization, agents

Use skills naturally. A keyword list helps ATS matching, but your bullets and projects should show how RAG, embeddings, vector databases, evals, or guardrails supported real LLM features.

See ai engineer resume keywords

AI Engineer Resume Bullet Point Examples

Strong AI engineer bullets explain the LLM feature you built, the retrieval, prompting, or eval technique you used, and the outcome for accuracy, hallucination rate, cost, or adoption.

Weak Example

Strong Example

Used the OpenAI API.

Built a customer-support assistant on the OpenAI API with a RAG pipeline that grounded answers in help docs and cut escalations to human agents.

Worked on RAG.

Built a RAG pipeline with pgvector embeddings and hybrid search that improved answer accuracy and added source citations to reduce hallucinations.

Did prompt engineering.

Designed and versioned prompt templates with function calling, then validated changes against an eval set before shipping to production.

Evaluated the model.

Built an eval pipeline with curated test cases that caught accuracy regressions, preventing two bad prompt changes from reaching users.

Reduced costs.

Cut LLM token cost by ~35% by tuning chunk sizes, caching retrievals, and routing simple queries to a smaller model without hurting quality.

Generate stronger bullet points See ai engineer resume bullet examples

AI Engineer Project Example

Support Agent with Tools

Stack: Python · LangChain · Pinecone · OpenAI API · FastAPI

Built an LLM agent that answers support questions and calls internal tools to look up orders. The project demonstrates retrieval, function calling, evaluation, and guardrails for a production-style AI feature.

Built a LangChain agent with function calling to query an internal order API and a knowledge base.
Stored embeddings in Pinecone and used semantic search to ground responses in real documentation.
Added an eval harness measuring task success and tool-call accuracy across scenarios.
Implemented guardrails and fallbacks for out-of-scope or low-confidence requests.

A strong AI project should show more than a chatbot demo. Explain the retrieval design, prompts or agent logic, evaluation method, and how you kept output safe and accurate.

See ai engineer resume project examples

Common Mistakes to Avoid

Only listing model names

Do not stop at GPT or Claude. Show the RAG pipelines, agents, or features you built and how they solved a real problem.

No evaluation

Recruiters increasingly look for evals and guardrails. Show how you measured accuracy and prevented regressions, not just that you prompted a model.

Ignoring hallucination and safety

LLM features fail without grounding and guardrails. Showing retrieval, citations, and safety handling makes your work credible.

No cost or latency awareness

Production LLM work is judged on cost and speed too. Mention token cost, caching, model routing, or latency where relevant.

AI Engineer ATS Checklist

Use a clean, single-column resume format.
Use standard section names like Summary, Skills, Experience, Projects, and Education.
Include AI engineering keywords from the job description when they match your real experience.
Avoid icons, complex tables, text boxes, and heavy graphics in the main resume content.
Show evidence for RAG, embeddings, evaluation, and production deployment in bullets or projects.
Use clear job titles, company names, dates, and locations.
Spell out terms like RAG (retrieval-augmented generation) once so they match keyword searches.
Export as PDF unless the employer specifically asks for DOCX.

How to Tailor This Resume to a AI Engineer Job Post

Do not send the same AI engineer resume to every company. Some roles focus on RAG and search, others on agents, fine-tuning, evaluation and safety, or productionizing LLM features at scale.

Step 1

Paste the job description

Start with the actual posting so you can see the required LLM stack, retrieval tools, and AI responsibilities that matter most.

Step 2

Identify AI priorities

Look for signals like RAG, embeddings, vector databases, LangChain, LlamaIndex, evals, guardrails, agents, fine-tuning, or model APIs.

Step 3

Match real experience

Choose bullets and projects that honestly support the role, especially the RAG, eval, and deployment work closest to the target job.

Step 4

Rewrite for relevance

Move the most relevant LLM features, retrieval design, and outcomes closer to the beginning of your bullets.

Step 5

Check ATS formatting

Make sure your resume is easy to parse and includes the most important matching AI keywords naturally.

FAQ

Can I use this AI engineer resume example on my resume?

Yes, but use it as a guide, not a script to copy. The strongest AI engineer resume reflects your real LLM features, RAG pipelines, evaluation work, and production outcomes.

What should an AI engineer resume include?

An AI engineer resume should usually include a short summary, relevant LLM and retrieval skills, professional experience, projects, education, and evidence of RAG, embeddings, evaluation, guardrails, and production deployment.

What is the difference between an AI engineer and ML engineer resume?

An ML engineer resume emphasizes training and serving custom models, while an AI engineer resume emphasizes building applications on top of LLMs with RAG, prompting, agents, evaluation, and APIs. Tailor the emphasis to the role.

Should AI engineers include projects?

Yes. Projects can show RAG pipelines, agents, embeddings, and evaluation end to end, which is especially valuable in a fast-moving field where hands-on LLM work matters.

Do I need fine-tuning experience to be an AI engineer?

Not always. Many AI engineering roles focus on RAG, prompting, and evaluation rather than fine-tuning. List fine-tuning only if you have done it; strong retrieval and eval skills carry most AI engineer resumes.

How do I make my AI engineer resume more ATS-friendly?

Use clear section headings, relevant AI keywords from the job description, and bullets that prove your skills with real LLM or RAG work. Spell out acronyms like RAG once, and avoid over-designed layouts that can hurt parsing.

Make this example work for your resume

Turn this AI engineer resume example into a tailored resume

Use the examples above as a starting point, then tailor your real experience to a specific AI engineering job description. resubldr helps you improve keyword alignment, rewrite bullets, and keep your resume grounded in what you actually did.

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