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technologyRemoteATS-Optimized 2025

AI Engineer
Resume Guide

ATS-optimized resume guide for ai engineer roles in Remote. Work from anywhere in the US with location flexibility and no commute.

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$140K

Remote avg

12

ATS keywords

19

Skills listed

5

Resume tips

Remote · Avg salary

$140K

$110K – $190K range

$110K$140K avg$190K

125,000+ active listings in Remote

Compensation

AI Engineer Salary Data

National salary range

$140Kavg / yr

$110,000 – $190,000

$110K$140K avg$190K
Entry: $110KMid: $140KSenior: $190K

By city · annual avg

National avg
$140K
San Francisco
$180K
New York
$170K
Seattle
$165K
Boston
$160K
Austin
$150K

Skills

Top AI Engineer Skills in 2025

Technical skills Larger = more in-demand

PythonMachine LearningDeep LearningTensorFlowPyTorchLLMsNLPComputer VisionMLOpsKubernetesModel DeploymentData Pipelines

Soft skills

Problem Solving
Research Mindset
Collaboration
Communication
Creativity
Critical Thinking
Curiosity

Emerging skills Trending

Generative AIHot
RAG SystemsHot
Agentic AIHot

ATS Optimization

AI Engineer ATS Keywords

These keywords were extracted from hundreds of real ai engineer job postings. Click any keyword to copy it — then weave it naturally into your resume to beat ATS parsers like Workday, Greenhouse, and Lever.

Tip: Include both full terms and acronyms — e.g. "Continuous Integration (CI/CD)"

Expert advice

How to Write a AI Engineer Resume

01

Highlight specific models and architectures (Transformers, CNNs, RNNs) you've implemented

02

Showcase deployment experience with ML platforms (SageMaker, Vertex AI, MLflow)

03

Quantify model performance (accuracy, latency, throughput) and business impact

04

Include experience with LLMs (GPT, Claude, LLaMA) and fine-tuning techniques

05

Demonstrate MLOps expertise with CI/CD for ML and model monitoring

Sample content

AI Engineer Resume Examples

ai_engineer_resume_2025.pdf

Alex Johnson

AI Engineer

phone: (555) 012-3456
email: alex.johnson@email.com
location: Remote

WORK EXPERIENCE

Acme Corp

Senior AI Engineer - Remote - 2022 – Present

Design, train, and deploy deep learning models for NLP and computer vision applications Build and maintain MLOps pipelines for model training, evaluation, and serving Fine-tune large language models (LLMs) for domain-specific applications (RAG, agents)

•

Developed AI-powered recommendation engine that increased user engagement by 35% ($12M revenue impact)

TechVentures Inc

AI Engineer - Austin, TX - 2019 – 2022

Optimize model inference for production requirements, achieving sub-50ms latency Collaborate with data scientists to transition research models to production

•

Reduced model inference cost by 60% through quantization and optimization techniques

PROFESSIONAL SUMMARY

Innovative AI Engineer with 5+ years of experience building and deploying production machine learning systems. Expert in LLM applications, computer vision, and MLOps. Deployed models serving 5M+ users with sub-100ms latency. Passionate about pushing the boundaries of applied AI to solve real-world problems.

EDUCATION

University of California, Berkeley

B.S. in Computer Science - Berkeley, CA - 2015 – 2019

SKILLS

Python
Machine Learning
Deep Learning
TensorFlow
PyTorch
LLMs
NLP
Computer Vision
MLOps
Kubernetes
Model Deployment
Data Pipelines
Problem Solving
Research Mindset
Collaboration
Communication

Measuring...

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Salary range

$140K

Remote avg

$190K

Senior level

$110K

Entry level

12

ATS keywords

Resume tips

01

Highlight specific models and architectures (Transformers, CNNs, RNNs) you've implemented

02

Showcase deployment experience with ML platforms (SageMaker, Vertex AI, MLflow)

03

Quantify model performance (accuracy, latency, throughput) and business impact

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ATS-optimized · 12 keywords · 2025
Professional Summary43 words

“Innovative AI Engineer with 5+ years of experience building and deploying production machine learning systems. Expert in LLM applications, computer vision, and MLOps. Deployed models serving 5M+ users with sub-100ms latency. Passionate about pushing the boundaries of applied AI to solve real-world problems.”

Key Responsibilities
  • Design, train, and deploy deep learning models for NLP and computer vision applications
  • Build and maintain MLOps pipelines for model training, evaluation, and serving
  • Fine-tune large language models (LLMs) for domain-specific applications (RAG, agents)
  • Optimize model inference for production requirements, achieving sub-50ms latency
  • Collaborate with data scientists to transition research models to production
Achievements · Quantified
  • Developed AI-powered recommendation engine that increased user engagement by 35% ($12M revenue impact)
  • Reduced model inference cost by 60% through quantization and optimization techniques
  • Led team of 4 engineers to build GenAI assistant that reduced customer support tickets by 28%

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Common questions

AI Engineer Resume FAQ

Essential skills for an AI engineer include Python programming, deep learning frameworks (TensorFlow, PyTorch), machine learning algorithms, MLOps practices, model deployment, and cloud platforms. Experience with LLMs, NLP, or computer vision is increasingly important, as is understanding of data pipelines and model optimization techniques.

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