AI engineer resume guide

AI Engineer Resume: Prove LLM Apps, RAG, and Production AI

Show generative AI applications, retrieval pipelines, model integration, and measurable outcomes that separate applied AI engineers from prompt tinkerers.

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LLM proof
RAG pipelines
Production AI
ATS-safe stack

Rahul Nair

AI Engineer

Hyderabad, India | rahul.nair@email.com | +91 98765 44007 | github.com/rahulnair-ai

Summary

AI engineer with 4 years building generative AI products. Shipped RAG-based support assistant cutting ticket handle time by 33%, fine-tuned models for domain Q&A, and deployed LLM features serving 1M monthly requests with cost controls.

Experience

AI EngineerMay 2022 - Present

Assist AI Platform

Built RAG support assistant with vector search over 80k docs, cutting average ticket handle time by 33%.
Deployed LLM features via FastAPI serving 1M monthly requests with caching and token-budget guardrails.
Reduced inference cost 28% through prompt compression, model routing, and response caching.

ML / AI EngineerJun 2020 - Apr 2022

Insight Labs

Fine-tuned transformer models for domain classification reaching 91% F1 on internal benchmark.
Built evaluation harness measuring hallucination and grounding for generated answers.

Applied generative AI
RAG, fine-tuning, and LLM integration show production AI, not only ChatGPT prompts.
Impact and cost metrics
Handle-time reduction, request volume, accuracy, and inference cost prove real value.
Engineering rigor
APIs, evaluation, guardrails, and deployment separate AI engineers from experimenters.
ATS-readable AI stack
List LLMs, RAG, LangChain, vector DBs, Python, MLOps in plain searchable text.

Sample summaries

AI engineer with 3 years building LLM chat assistants, RAG pipelines, and prompt evaluation systems.
Generative AI engineer skilled in fine-tuning, embeddings, and vector search for enterprise search.
Applied AI engineer experienced in agent workflows, tool calling, and LLM cost optimization.

Skills examples

GenAI: LLMs, RAG, fine-tuning, embeddings, prompt engineering, agents, evaluation
Tools: LangChain, LlamaIndex, vector databases (Pinecone, FAISS), OpenAI, Hugging Face
Engineering: Python, FastAPI, Docker, MLOps, monitoring, cost optimization

Experience bullets

Built tool-calling agent automating data lookups, reducing manual analyst steps by 50%.
Implemented hybrid search combining keyword and vector retrieval, improving answer relevance by 19%.
Created LLM eval pipeline scoring grounding and tone before each prompt release.

Show production AIRAG, deployment, and guardrails beat prompt experiments with no users.
Quantify impact and costHandle time, accuracy, request volume, and inference cost are strong AI metrics.
Include evaluationHallucination and grounding evaluation signal serious applied AI work.
Name the stackLangChain, vector DBs, and model providers help recruiters match roles.
Separate from ML engineerAI engineers emphasize LLM apps and integration; ML engineers emphasize training and serving infra.

What should an AI engineer resume include?

Include LLM/generative AI work, RAG, tools, deployment, impact metrics, evaluation, and education.

How do I write generative AI resume bullets?

State the AI feature, technique (RAG, fine-tuning, agents), and result such as faster resolution or cost cut.

AI engineer vs ML engineer resume?

AI engineers focus on LLM applications and integration. ML engineers focus on training, pipelines, and serving infrastructure.

Do I need fine-tuning experience?

Helpful but not required. RAG, prompt engineering, and deployment are valuable for many AI roles.

How long should an AI resume be?

One page early career. Two pages for senior engineers with multiple shipped AI products.

How can AI resumes pass ATS?

Use exact terms: LLM, RAG, generative AI, LangChain, vector database in plain text.

Build an AI engineer resume that proves production AI impact

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AI Engineer Resume: Prove LLM Apps, RAG, and Production AI

Clean enough for ATS. Polished enough for recruiters.

Rahul Nair

Summary

Experience

Built for scanners, humans and hiring intent.

Applied generative AI

Impact and cost metrics

Engineering rigor

ATS-readable AI stack

Copy the structure, not the wording.

Sample summaries

Skills examples

Experience bullets

Small edits that lift response rates.

Common questions, direct answers.

Build an AI engineer resume that proves production AI impact

AI Engineer Resume: Prove LLM Apps, RAG, and Production AI

Clean enough for ATS. Polished enough for recruiters.

Rahul Nair

Summary

Experience

Built for scanners, humans and hiring intent.

Applied generative AI

Impact and cost metrics

Engineering rigor

ATS-readable AI stack

Copy the structure, not the wording.

Sample summaries

Skills examples

Experience bullets

Small edits that lift response rates.

Common questions, direct answers.

Build an AI engineer resume that proves production AI impact