LLM Engineer Resume Example: RAG, Fine-Tuning, and GenAI

An LLM engineer resume is not a machine learning resume with a few buzzwords swapped in. The hiring bar has moved. Teams building on large language models (LLMs) want proof you can ship retrieval systems, tune models on real data, wire up APIs under production load, and keep latency and cost in check. That is a different story than "I trained a classifier and got 94% accuracy."

This page gives you a full, copyable LLM engineer resume example, then breaks down exactly what makes it work: the RAG and fine-tuning bullets, the GenAI tech stack keywords recruiters scan for, the metrics that prove impact, and the over-hyping mistakes that get strong candidates screened out. It works whether your title is LLM engineer, generative AI engineer, prompt engineer, or AI software engineer, since those postings ask for overlapping skills.

Traditional machine learning roles reward model-building from scratch: feature engineering, training pipelines, hyperparameter tuning, offline evaluation. LLM engineering is mostly the opposite. You are usually building around a foundation model you did not train, and the hard problems live in retrieval, prompting, orchestration, evaluation, and serving.

That shift changes what belongs on the page. A recruiter filling an LLM role is looking for signals like these:

If your resume reads like a 2021 ML resume (lots of scikit-learn and "built a deep learning model"), it will look dated for these roles. Lead with the LLM-specific work and let the classical ML sit underneath as supporting depth. For the broader mechanics of a strong tech resume, the ATS-friendly software engineer resume guide pairs well with everything here.

Here is a full mid-level example you can adapt. It targets an LLM / generative AI engineer role at a product company. Read the bullets closely: each one names a technology, describes an action, and ends with a measurable result.

Priya Menon
LLM Engineer | RAG, Fine-Tuning & GenAI Systems
San Francisco, CA · priya.menon@email.com · linkedin.com/in/priyamenon · github.com/priyamenon

Summary. LLM engineer with 4 years building production generative AI systems. Shipped a RAG-based support assistant serving 40K daily queries at p95 latency under 1.2s, and cut inference cost 38% through caching and model routing. Comfortable across the stack: retrieval, fine-tuning (LoRA/QLoRA), evaluation, and serving.

Senior AI Engineer — Fintech SaaS Co. · 2023–Present

• Built a retrieval-augmented generation pipeline over 2M internal documents using a vector database and hybrid search, raising answer relevance from 71% to 89% on a 500-question eval set.
• Reduced hallucinated responses by 46% by adding a reranking step and citation-grounded prompting, measured through weekly human review.
• Cut per-query cost 38% by routing simple intents to a smaller model and caching embeddings, saving roughly $18K/month at current volume.
• Fine-tuned an open-weight model with LoRA on 12K labeled support tickets, improving tone-match scores while keeping the base model frozen for other tasks.

Machine Learning Engineer — Analytics Startup · 2021–2023

• Shipped an LLM-powered document summarization API handling 8K requests/day with a 99.9% uptime SLA.
• Designed the evaluation harness (accuracy, latency, cost per 1K tokens) that became the team standard for shipping model changes.
• Integrated OpenAI and Anthropic APIs behind a provider-agnostic layer so the team could switch models without rewriting application code.

Projects. Open-source RAG evaluation toolkit (1.2K GitHub stars); 2nd place, GenAI Hackathon 2024 for an agentic research assistant.

Skills. Python, PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, vector databases (Pinecone, Weaviate, pgvector), RAG, LoRA/QLoRA fine-tuning, prompt engineering, model evaluation, FastAPI, Docker, AWS.

Education. B.S. Computer Science, UC Davis.

Notice what this example does not do: no "expert in AI," no laundry list of every model name, no unquantified claims. Every line a recruiter cares about carries a number or a concrete technology. Save and send it as a PDF so the formatting holds up in every inbox and parser.

These three areas are the core of most LLM job descriptions, so give them the most detail. The trick is to describe the engineering decision, not just the tool.

Weak: "Built a RAG chatbot with LangChain." That tells a recruiter nothing about scale or quality. Strong bullets name the retrieval strategy and the outcome:

• "Implemented hybrid search (BM25 + dense embeddings) with reranking, lifting top-3 retrieval precision from 62% to 84%."
• "Reduced hallucinations by grounding responses in retrieved passages and requiring inline citations, cutting escalations to human agents by a third."
• "Tuned chunk size and overlap across 2M documents, cutting irrelevant context tokens 25% and lowering cost per query."

Fine-tuning is often the wrong tool, and senior interviewers know it. Show that you chose it deliberately. Mention the method (LoRA, QLoRA, full fine-tune, instruction tuning), the dataset size, and what improved. A line like "fine-tuned with LoRA on 12K labeled examples after prompting and RAG hit a ceiling" reads far stronger than "fine-tuned an LLM," because it proves you tried cheaper options first.

Production LLM work is mostly integration: calling model APIs, handling rate limits, streaming responses, managing failover between providers. Call out patterns that show maturity, like a provider-agnostic abstraction layer, retry-and-backoff logic, or token budgeting. These signal you have run LLMs in production, not just in a notebook.

Applicant tracking systems (ATS) and recruiters both scan for a specific vocabulary in GenAI postings. Two categories have surged in job descriptions over the past year: vector databases and orchestration frameworks. A resume that mentions RAG but names no vector store or framework looks thin next to one that lists the actual tools.

Across the GenAI job descriptions we process, the fastest-growing keyword clusters are exactly these. Postings that a year ago said "experience with LLMs" now ask for named vector databases (Pinecone, Weaviate, pgvector, Chroma, Milvus) and named orchestration or agent frameworks (LangChain, LlamaIndex, LangGraph, and increasingly agent tooling). If you have used them, name them. If you have used only one, name that one honestly and describe what you built.

Here is a practical map of what to include, grouped so you can mirror it in your skills section.

Do not paste this whole table into your resume. Pick the tools you genuinely used and weave them into bullets and a focused skills line. Keyword stuffing backfires: it trips ATS relevance scoring and it reads as padding to a human. For the method behind matching keywords to a specific posting, see ATS resume keywords and how to beat the ATS.

The single biggest upgrade you can make to an LLM resume is attaching numbers to your work. GenAI has clean, credible metrics, and using them separates engineers who shipped from engineers who experimented. Focus on three families.

If you cannot cite an exact figure, use an honest estimate framed as one ("cut cost roughly a third") rather than a fake precise number. Recruiters spot invented metrics fast, and fabricated numbers collapse in interviews. A grounded estimate beats both a made-up stat and a bullet with no number at all.

Frame each result against a baseline. "Improved relevance from 71% to 89%" tells a story; "achieved 89% relevance" is just a snapshot. The delta is the value you created.

GenAI is one of the few fields where public work carries real weight, because the ecosystem is young and mostly open source. A merged pull request to a popular LLM framework, a widely-forked RAG demo, or a hackathon win is concrete proof you can build, and it partly compensates for a short work history.

Treat these like mini-experience entries. Name the project, your specific contribution, and any traction (stars, downloads, placement). "Contributed a caching module to an open-source LLM orchestration library, merged and shipped in v0.4" says more than "active open-source contributor."

Put your GitHub front and center, and make sure the linked repos are actually presentable. For placement and how to link it cleanly, see how and where to include your GitHub link, and for framing side projects as evidence, how to list coding projects on a resume.

If you are early-career or moving into GenAI from adjacent software work, projects and hackathons carry your resume. Build one or two end-to-end LLM projects you can describe with real metrics: a RAG app over a public dataset, an agent that automates a genuine task, an eval harness. Deploy it, measure it, and write the bullets like the example above. A junior LLM engineer resume with two deployed, measured projects beats a senior title with vague claims.

The fastest way to lose credibility on an LLM resume is to oversell. Hiring managers in this space are technical and skeptical, because the field is full of hype. These are the mistakes that get strong candidates rejected:

The through-line: specificity reads as competence, and vagueness reads as bluffing. For a wider list of what to avoid, see common resume mistakes.

GenAI titles and stacks are inconsistent, so one generic resume will underperform. A "generative AI engineer" posting might center RAG and product work; an "AI software engineer" role might weight backend integration; a research-leaning role might want fine-tuning and evaluation depth. Reorder your bullets and skills to match the emphasis of the specific posting.

This is slow to do by hand for every application, and being early in the pipeline matters. Roleframe keeps one base LLM-engineer resume and spins off a version tailored to each job description in seconds, matching the keywords and section order that posting actually asks for. If you maintain several targets (LLM engineer, AI software engineer, prompt engineer), managing multiple resumes with separate role workspaces keeps them from bleeding together.