AI Engineer Resume Example and Template
A strong AI engineer resume shows hands-on experience building and deploying machine learning models in production. Include specific models you've trained, frameworks you've mastered, and quantifiable business impact like improved accuracy, reduced latency, or revenue generated.
Hiring managers look for engineers who understand model training, evaluation, and deployment constraints. Demonstrate both research depth and production engineering skill, including how you've handled real-world data challenges.
A complete, realistic AI Engineer resume, laid out in an ATS-tested template. The candidate is illustrative: the name, employers and figures were written for this example and identify no real person, so you can copy its structure and wording freely.
Updated September 2026
Also searched as
Senior AI Engineer, Machine Learning Engineer, Junior Machine Learning Engineer
Priya Desai: AI Engineer
San Francisco, California, United States
Summary
AI engineer with 7 years building and deploying machine learning systems at scale. Specialized in computer vision and natural language processing using PyTorch and TensorFlow. Built fraud detection system reducing false positives by 41% while catching 28% more fraud. Led team of 3 engineers developing production ML pipelines serving 2M+ daily inference requests.
Work Experience
Senior AI Engineer, DataFlow Systems
Mar 2023 – Present. Financial services platform processing $500M+ annual transactions
- Led development of real-time fraud detection system using gradient boosted decision trees and neural networks, reducing false positive rate by 41% while improving fraud catch rate by 28%
- Built end-to-end ML pipeline using Python, Spark, and Airflow processing 5TB daily transaction data, training models nightly and deploying new versions with zero downtime
- Mentored team of 2 junior engineers on production ML best practices, code review, and model evaluation methodology, improving team code quality and reducing production incidents by 35%
- Designed A/B testing framework for model evaluation, enabling data-driven decisions on model improvements with statistical rigor and business impact measurement
Machine Learning Engineer, Horizon AI Research
Aug 2020 – Feb 2023. AI research company focused on computer vision and robotics
- Developed object detection models using YOLO and Faster R-CNN, achieving 94% mAP on custom dataset and deploying to edge devices for real-time inference
- Built data pipeline for collecting, labeling, and augmenting 200K training images, establishing data quality standards and reducing labeling errors by 12%
- Optimized model inference latency using quantization and pruning, reducing model size by 60% with only 2% accuracy drop, enabling mobile deployment
- Published research on few-shot learning for object detection, contributing to 2 peer-reviewed conference papers and 1 open-source library with 500+ GitHub stars
Junior Machine Learning Engineer, TechCore AI Solutions
Jul 2019 – Jul 2020. Startup building NLP and recommendation systems for e-commerce
- Trained and evaluated NLP models for sentiment analysis and product recommendation using PyTorch and scikit-learn on datasets of 1M+ examples
- Built data preprocessing pipeline handling 2M e-commerce product listings, implementing feature engineering and normalization for model training
- Collaborated with product team to A/B test recommendation model improvements, leading to 8% increase in average order value from better personalization
- Contributed to open-source NLP library and wrote technical blog posts on model evaluation techniques, building visibility and expertise in the field
Education
Stanford University
Master of Science, Computer Science, Artificial Intelligence. Sep 2017 – Jun 2019
University of California, Davis
Bachelor of Science, Computer Science. Sep 2013 – May 2017
Skills
Python, PyTorch, TensorFlow, Computer Vision, Natural Language Processing, Model Training and Evaluation, MLOps, Spark, Data Pipeline Development, Statistical Analysis
Languages
English, Hindi
Why this look works
AI and machine learning roles demand a cutting-edge, modern aesthetic. A clean, minimalist design with sophisticated typography and color choices reflects the innovation and precision expected in AI work. Organized layout makes it easy for technical hiring committees to scan research output and project scope.
Resume tips for this job
- Lead with quantifiable model performance: accuracy, F1 score, latency, or inference throughput matter more than training loss or theoretical understanding
- Show production impact, not just research: companies hire engineers who ship working systems, not just papers. Emphasize deployment, A/B testing, and real-world constraints
- Name specific frameworks and libraries you've mastered: PyTorch, TensorFlow, Spark, scikit-learn, and specific computer vision or NLP libraries all matter
Common questions
Should I include research papers if I haven't published in top conferences?
Include peer-reviewed publications regardless of venue. If you have strong preprints or internal research, mention those briefly. Hiring managers care more about demonstrating ML depth than conference prestige.
How much Python coding depth should I show on a resume?
Emphasize production engineering over algorithmic depth. You need strong coding skills, but hiring managers want evidence you can write maintainable, tested code in production ML pipelines, not just notebooks.