Resume Skills / AI Engineer

AI Engineer Resume Skills: 80+ skills for your resume

The LLM and generative-AI skills hiring managers and ATS actually scan for in 2026 — whether the role is called AI engineer, LLM engineer or generative AI engineer. Foundation models, RAG, prompt engineering, orchestration and deployment, organized by category and experience level, with real examples of how to phrase them. Explore the list, or generate a set tailored to you.

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Most-listed ai engineer skills
PythonLLMsRAGPrompt EngineeringLangChainVector DatabasesOpenAI APIFine-TuningEmbeddingsPyTorch
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What skills should an AI engineer put on a resume?

AI engineer and LLM engineer roles are about building products on top of foundation models — reliably and in production. Lead with Python and hands-on LLM work (OpenAI/Anthropic APIs, prompt engineering), retrieval-augmented generation (RAG) with a vector database, and an orchestration framework (LangChain or LlamaIndex), then the production side — deployment, evaluation and monitoring. As you grow, add fine-tuning, agents, multimodal models and inference optimization. Tailor the exact mix to the job you're targeting.

The 10 essentials, in order
01Python02LLMs03RAG04Prompt Engineering05LangChain06Vector Databases07Embeddings08APIs09Fine-Tuning10Model Deployment
Languages & CoreLLMs & Foundation ModelsRAG & RetrievalPrompt EngineeringFrameworks & AgentsFine-Tuning & TrainingDeployment & LLMOpsSafety & Responsible AISoft Skills
✦ The full list

Essential ai engineer skills

Tap any skill to select it, copy one, or grab a whole category — then paste straight into your resume.

Show skills for

Languages & Core

7 skills

Lead with Python — the default for AI work — and add the others you genuinely build with, e.g. “TypeScript (AI SDK, Next.js)”.

LLMs & Foundation Models

9 skills

The heart of the role — list the models and providers you've actually built with, and add depth in parentheses, e.g. “Claude (tool use, long context)”.

RAG & Retrieval

10 skills

The most common LLM system pattern — name the vector stores and retrieval techniques you've shipped.

Prompt Engineering

8 skills

The craft of getting reliable output from models — claim the techniques you've actually used to raise quality.

Frameworks & Agents

8 skills

How you wire models into applications — list the orchestration tools and agent patterns you've built with.

Fine-Tuning & Training

8 skills

The deeper model work that sets senior AI engineers apart — claim what you've actually run, not just read papers on.

Deployment & LLMOps

10 skills

Turning demos into products — name the tools you've used to ship, evaluate and monitor LLM systems.

Safety & Responsible AI

6 skills

Even a couple of these signal you build carefully — list what you've actually implemented, not just heard of.

Soft Skills

10 skills

Choose 3–5 you can demonstrate through real work examples, not just claim — prove them in your experience bullets.
✦ Interactive · free

Generate ai engineer skills with AI

Pick a focus and your level — we'll suggest a tailored, ATS-ready skill set you can select from and copy straight into your resume.

Skill type
Experience level
How many skills · up to 10 per focus
Rarity · common ↔ niche
Job description · optional, improves relevance
10 balanced skills · tailored to type + level
✦ Progression

AI Engineer skills by experience level

As you grow, the emphasis shifts from building features to owning systems and people. Here's what belongs on your resume at each stage.

Entry Level

Build with the APIs
Python
OpenAI API
Prompt Engineering
RAG
Embeddings
Git

Mid-Level

Ship LLM features
LangChain
Vector Databases
AI Agents
Fine-Tuning
LLM Evaluation
Docker

Senior

Own AI systems in production
Multi-Agent Systems
LoRA / PEFT
Inference Optimization
LLMOps
Cost Optimization
Technical Leadership
FoundationalOwnership & scope
✦ Placement

Where to put ai engineer skills on your resume

Skills shouldn't live in one list at the bottom. Strong resumes weave them through three places — here's each, with a real example.

01Skills section
SKILLS
Python ·LLMs ·RAG ·LangChain ·Vector Databases ·Fine-Tuning
A tight, scannable list that balances LLM building blocks and production — not every model you've prompted once.
02Professional summary
SUMMARY
AI Engineer with 3+ years building LLM-powered products — RAG pipelines over vector databases and agentic workflows with LangChain — shipped to production with evaluation and monitoring.
Fold your top 2–3 skills into a sentence that frames your experience and focus.
03Work experience
EXPERIENCE · BULLET
Cut support resolution time 35% by building a RAG assistant over the docs with pgvector and reranking, plus LLM evaluation to keep answer quality above 90%.
The most convincing place — show a skill delivering a measurable result.
✦ Specificity wins

Make your skills more specific

A bare keyword tells a recruiter nothing. A specific one shows depth — and matches more of the phrases an ATS looks for.

BasicLLMs
BetterLLMs — OpenAI & Claude APIs, tool calling, structured output, streaming
BasicRAG
BetterRAG — pgvector retrieval, chunking, hybrid search and reranking, eval-driven
BasicPrompt engineering
BetterPrompt engineering — few-shot, chain-of-thought, guardrails, measured on an eval set
✦ Tailoring

Match your skills to the job description

ATS rank you partly on how well your resume echoes the posting. Mirror the skills a job names — in your own true words — and prioritize them near the top.

Job description excerpt

"Seeking an AI engineer experienced with LLMs, RAG and vector databases, prompt engineering, LangChain, and deploying AI features to production, comfortable evaluating and monitoring model quality."

Skills to prioritize
LLMs
RAG / Vector Databases
Prompt Engineering
LangChain
Model Deployment
✦ Avoid these

Common resume skills mistakes

i
Listing "ChatGPT" as a skill. Using a chatbot isn't engineering. Show APIs, RAG, orchestration and production work instead.
ii
Claiming AI impact you can't quantify. "Built an AI feature" is empty. Attach numbers — accuracy, latency, cost per call, business metric moved.
iii
All demos, no production. Notebooks and hackathon prototypes don't prove you can ship. Show evaluation, monitoring and reliability.
iv
Buzzword-stuffing every AI term. Listing RLHF, agents and fine-tuning you've never run is transparent in an interview. Claim what you've built.
v
Skipping the software engineering fundamentals. AI engineers write production code. Python, APIs, Git and Docker matter as much as the model choice.
vi
Listing skills you never demonstrate in your experience. Back each headline skill with a bullet that proves it in a shipped system.
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✦ FAQ

Frequently asked questions

Aim for 14–18 well-chosen skills. Enough to cover Python, LLM APIs, RAG and vector search, an orchestration framework, and the production/evaluation side — but curated to what you can actually discuss. A wall of 80 buzzwords reads as noise and dilutes your strongest signals.
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