Tech Track
Mid to Senior
Layout: 1-Page Compact ATS
AI & Machine Learning Engineer Resume Example & ATS Guide (2026)
AI & Machine Learning Engineer with 5+ years developing production Generative AI applications, fine-tuned LLM services, and Retrieval-Augmented Generation (RAG) architectures. Built enterprise agent systems serving 2M+ queries with 94% retrieval accuracy and sub-200ms latency.
99.3%
Workday ATS Pass
99.7%
Greenhouse ATS Pass
Google X-Y-Z
Bullet Formula
Top ATS Keywords & Technical Skills for AI & Machine Learning Engineer
Large Language Models (LLMs)PyTorchLangChain & LlamaIndexRAG ArchitectureVector Databases (Pinecone, Qdrant)vLLMFastAPILoRA Fine-TuningDockerPython 3.12
ATS-Compliant AI & Machine Learning Engineer Resume Sample (Plain-Text Preview)
Professional Summary
AI & Machine Learning Engineer with 5+ years developing production Generative AI applications, fine-tuned LLM services, and Retrieval-Augmented Generation (RAG) architectures. Built enterprise agent systems serving 2M+ queries with 94% retrieval accuracy and sub-200ms latency.
Core Technical Skills
Languages & Core: Large Language Models (LLMs), PyTorch, LangChain & LlamaIndex, RAG Architecture
Frameworks & Tools: Vector Databases (Pinecone, Qdrant), vLLM, FastAPI
Cloud, Databases & Architecture: LoRA Fine-Tuning, Docker, Python 3.12
Work Experience
Key Engineering Projects
Enterprise Multi-Agent RAG Orchestration Framework (Python 3.12, LangGraph, Qdrant, FastAPI, Docker, Llama 3)
- Constructed multi-agent reasoning framework with dynamic router agents, web search fallback, and self-corrective retrieval reflection loops.
- Benchmarked against open-domain benchmarks achieving 89.2% accuracy on complex multi-hop question answering.
Education
M.S. in Artificial Intelligence & Computer Science
Stanford University | Stanford, CA (2018 - 2020)
Certifications & Credentials
- DeepLearning.AI Generative AI Specialist
- AWS Certified Machine Learning – Specialty
How to Write a High-Scoring AI & Machine Learning Engineer Resume
- Use Quantified Google X-Y-Z Bullets: Start each bullet with a strong action verb (Architected, Engineered, Optimized) and quantify the outcome with real metrics (reduced latency by 42%, cut cloud spend by $180k).
- Align Core Keywords to the Job Description: Ensure technologies like Large Language Models (LLMs), PyTorch, LangChain & LlamaIndex, RAG Architecture appear in both your Skills inventory and your experience bullets.
- Keep Parser Hierarchy Clean: Avoid tables, multi-nested graphics, or un-selectable text that trip up Workday and Taleo scanners.
- Click "Use This Resume in AI Builder" to personalize this sample and download a polished, vector-sharp PDF in seconds.
Frequently Asked Questions
What skills are most in-demand for an AI Engineer resume in 2026?
RAG architecture (Retrieval-Augmented Generation), vector databases (Pinecone, Qdrant), LLM fine-tuning (LoRA, QLoRA), inference optimization (vLLM, TensorRT-LLM), and evaluation frameworks (RAGAS).
How should prompt engineering and LLM work be quantified on a resume?
Highlight reduction in token latency (TTFT), retrieval accuracy lifts (e.g. 71% to 94%), cost reductions from hosting open models vs proprietary APIs, and hallucination reduction metrics.
Is CareerAI PRO's AI Engineer template ATS compliant?
Yes, the "AI & ML Core Lead" template achieves 99.3% Workday and 99.7% Greenhouse pass rates while providing prominent callouts for research and production model architectures.