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Resume Example

AI Engineer Resume Example

Focus on production AI systems, RAG pipelines, and evaluation. This sample highlights model impact and reliability.

Modern Minimal

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Keywords

PythonTensorFlow/PyTorchHugging FaceLangChainOpenAI APIsVector databasesNLPComputer vision

Sample bullets

  • Built a RAG pipeline that improved answer accuracy by 28%.
  • Deployed an agentic workflow that reduced support resolution time by 35%.
  • Implemented evaluation harnesses that cut hallucination rate by 22%.

Soft skills

  • Problem framing
  • Experimentation
  • Collaboration
  • Responsible AI mindset

Certifications

  • AWS ML Specialty
  • Google Cloud Professional ML Engineer
  • TensorFlow Developer Certificate

Why this works

  • Connects AI work to measurable product outcomes.
  • Shows production readiness and monitoring discipline.
  • Highlights responsible AI practices.

Step-by-Step Guide

How to Write a AI Engineer Resume

1

Lead with AI systems in production

AI engineers build AI-powered products. Lead with AI applications deployed, user scale, and business impact. 'AI Engineer building conversational AI handling 1M daily interactions with 90% resolution rate.'

2

Show LLM and foundation model expertise

Include experience with LLMs (GPT, Claude, Llama), fine-tuning, prompt engineering, RAG, and embedding systems. Modern AI engineering centers on foundation models.

3

Highlight production AI challenges

Include experience with latency optimization, cost management, evaluation, guardrails, and reliability. Production AI requires solving challenges beyond model selection.

4

Include infrastructure and MLOps

Describe AI infrastructure: vector databases, model serving, monitoring, and evaluation pipelines. AI systems require specialized infrastructure.

5

Demonstrate responsible AI practices

Include experience with safety, bias mitigation, and responsible AI deployment. Trust and safety are critical for AI systems.

Summary Examples

Good vs. Bad Resume Summaries

✓ Good

AI Engineer building production LLM applications for enterprise. Deployed RAG system reducing support tickets 40% for 100K users. Optimized inference costs 60% through caching and model distillation. Expert in LangChain, vector databases, and prompt optimization.

Production scale, business impact, cost optimization, and modern AI stack.

✗ Bad

AI enthusiast experienced with ChatGPT and machine learning. Passionate about artificial intelligence applications.

Using ChatGPT isn't engineering. No production systems or technical depth.

✓ Good

Senior AI Engineer leading conversational AI platform handling 5M monthly users. Built evaluation framework improving response quality 35%. Implemented guardrails reducing harmful outputs 99%. Fine-tuned models for domain-specific performance.

Scale, evaluation, safety focus, and fine-tuning experience.

✗ Bad

Developer interested in AI seeking AI engineer role. Built chatbot using GPT API for personal project.

Personal project with API doesn't demonstrate AI engineering depth.

Action Verbs

Power Words for AI Engineer Resumes

BuiltDeployedDevelopedImplementedOptimizedFine-tunedDesignedIntegratedEvaluatedReducedImprovedScaledLedCreatedEngineeredArchitectedMonitoredTrainedTestedLaunched

Common Mistakes

What to Avoid

  • Confusing AI usage with AI engineering
  • Not specifying production scale and impact
  • Omitting LLM and foundation model expertise
  • Being vague about AI infrastructure experience
  • Not showing evaluation and quality practices
  • Missing cost optimization and efficiency

Salary ranges

LevelUSEUCanada
EntryUSD 100,000-140,000EUR 50,000-70,000CAD 100,000-140,000
MidUSD 150,000-200,000EUR 70,000-120,000CAD 140,000-160,000
SeniorUSD 200,000-450,000EUR 90,000-140,000CAD 150,000-180,000

Market themes

  • Agentic AI postings surged
  • Average US salary reached about USD 206,000

US hot markets

  • San Francisco
  • Seattle
  • New York
  • Boston
  • Los Angeles

EU hot markets

  • London
  • Berlin
  • Munich
  • Amsterdam

Canada hot markets

  • Toronto
  • Montreal
  • Vancouver

FAQ

Common questions about this role

What should AI engineers emphasize?

Model impact, production readiness, and evaluation rigor.

Which metrics matter most?

Accuracy lift, latency, and quality regression prevention.

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