Resume Example & Template

AI Engineer Resume Example

What the role involves, a real resume example, and what employers screen for — then build your own in minutes.

Build your AI Engineer resume

What is a AI Engineer?

An AI Engineer builds, deploys, and maintains machine-learning and artificial-intelligence systems that solve real business problems. Sitting between data science and software engineering, they take models from experiment to production — increasingly working with generative AI and large language models (LLMs) as well as classic ML.

The role is hands-on and end-to-end: designing data pipelines, training or fine-tuning models, building retrieval and prompt systems, and deploying everything reliably at scale (MLOps). Strong AI Engineers pair solid software fundamentals with applied ML knowledge and a sharp focus on measurable outcomes.

Key skills for a AI Engineer resume

  • Python & production-grade software engineering
  • ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • Generative AI & LLMs (fine-tuning, RAG, prompt engineering)
  • MLOps (model deployment, monitoring, pipelines)
  • Cloud & data infrastructure (AWS/GCP/Azure)
  • Data engineering & feature pipelines
  • Math & statistics fundamentals
  • Evaluation, experimentation & A/B testing

AI Engineer resume example

Ravi Menon

AI Engineer | Machine Learning & Generative AI

Amsterdam, Netherlands

Summary

AI Engineer with 6+ years building and shipping machine-learning and generative-AI systems to production. Blends strong software engineering with applied ML — from data pipelines and model fine-tuning to RAG and MLOps — with a track record of turning models into reliable, measurable products.

Experience

Senior AI Engineer · Adyen

Mar 2021 – Present

  • Built and deployed LLM-powered features (RAG over proprietary data, fine-tuned models) into production, serving millions of requests.
  • Designed MLOps pipelines (training, evaluation, monitoring) that cut model deployment time from weeks to days.
  • Improved a core fraud-detection model’s precision 18% and reduced inference latency 45% through optimization.

Machine Learning Engineer · TomTom

Aug 2018 – Feb 2021

  • Developed computer-vision and NLP models and the data pipelines feeding them on AWS.
  • Shipped an ML-driven feature adopted by 500k+ users, with rigorous offline and online evaluation.

Education

MSc Artificial Intelligence

University of Amsterdam · 2016 – 2018

BSc Computer Science

Delft University of Technology (TU Delft) · 2013 – 2016

Certifications

  • DeepLearning.AI — Deep Learning Specialization
  • AWS Certified Machine Learning — Specialty
  • TensorFlow Developer Certificate

Skills

ML & AI: PyTorch · TensorFlow · LLMs · RAG · Fine-tuning · Prompt Engineering

Engineering: Python · SQL · FastAPI · Docker · Kubernetes

MLOps & Cloud: AWS · MLflow · Airflow · Vector DBs · Model Monitoring

Use this as your starting point

How to write a AI Engineer resume that stands out

  • Show models in production, not just notebooks — deployment, monitoring, and real impact separate AI Engineers from researchers.
  • Name the modern stack explicitly (PyTorch, LLMs, RAG, vector databases, MLOps tools); ATS and technical screeners match on them.
  • Quantify outcomes — accuracy gains, latency cut, cost saved, users served, revenue influenced.
  • Include notable projects with a clear problem → approach → result, ideally with a GitHub or paper link.
  • Balance ML depth with software engineering rigor — clean code, testing, and scalable systems.

AI Engineer resume — FAQ

What does an AI Engineer do?

An AI Engineer builds and deploys machine-learning and AI systems in production — designing data pipelines, training or fine-tuning models (including LLMs), building retrieval and prompt workflows, and running everything reliably at scale. They turn AI capabilities into working products with measurable business impact.

What skills do you need to be an AI Engineer?

Strong Python and software engineering, ML frameworks (PyTorch, TensorFlow), generative-AI and LLM techniques (fine-tuning, RAG, prompt engineering), MLOps for deployment and monitoring, cloud and data infrastructure, and solid math and statistics — plus rigorous evaluation and experimentation.

How is an AI Engineer different from a Data Scientist?

A Data Scientist focuses on analysis, modeling, and insight — often in notebooks. An AI Engineer focuses on building and productionizing AI systems: reliable pipelines, deployed models, and scalable software. There is overlap, but AI Engineering leans more toward engineering and shipping to production.

What should an AI Engineer resume include?

A summary framing you as a production-focused AI/ML builder, experience bullets pairing what you built with results, a clearly listed modern stack (Python, PyTorch, LLMs, MLOps, cloud), notable projects with outcomes, education, and relevant certifications. Keep it ATS-safe with real text.

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