AI Engineer CV-példa

Choose the right format for an AI Engineer resume

Use a reverse-chronological format. Hiring managers at tech companies and research labs want to see your most recent work first, and applicant tracking systems parse this layout most reliably. Keep the document to one page if you have under ten years of experience; two pages are acceptable for senior or staff-level roles with substantial publication or project histories.

Set margins to 0.75–1 inch, use a clean sans-serif font like Calibri or Inter at 10–11pt, and avoid tables or multi-column layouts that break ATS parsing. Place your name, LinkedIn URL, GitHub profile, and a portfolio or Hugging Face link in the header — recruiters at companies like Google DeepMind, OpenAI, or mid-size MLOps shops will click those links before the phone screen.

• Reverse-chronological order, most recent role first
• One page for under 10 years; two pages for senior/staff roles
• Include GitHub and portfolio links in the header
• No tables, text boxes, or graphics that confuse ATS
• File format: PDF unless the job posting specifies otherwise

Write a professional summary that lands interviews

Your summary sits directly below your contact information and should be three to four sentences that tell a recruiter exactly what you build, at what scale, and with which stack. Avoid vague phrases like 'passionate about technology.' Name the frameworks, model types, and business outcomes you deliver.

Here is an example of a strong summary: "ML Engineer with 6 years building and deploying production NLP and computer vision systems on AWS SageMaker and GCP Vertex AI. Reduced model inference latency by 38% at a Series B fintech by migrating pipelines from batch to real-time using Kafka and TensorFlow Serving. Experienced in fine-tuning large language models with RLHF and PEFT techniques, and in collaborating with product and data teams to ship models that move revenue metrics."

Write work-experience bullets that show impact

Each bullet should follow an action-verb → task → quantified result structure. Recruiters spend seconds scanning; numbers make bullets scannable and credible. Focus on model performance improvements, inference cost reductions, training time cuts, dataset scale, and downstream business metrics — not just the tools you used.

Avoid listing responsibilities. 'Responsible for maintaining ML pipelines' tells a hiring manager nothing. Show what you changed and what happened because of it.

• Rebuilt a real-time fraud detection model using XGBoost and feature stores on Feast, reducing false-positive rate by 22% and saving the business an estimated $1.4M annually in manual review costs.
• Designed and deployed a multi-modal image-text retrieval system using CLIP and FAISS on Kubernetes, cutting search latency from 420ms to 95ms at 10M daily queries while holding GPU spend flat.

Skills and certifications recruiters and ATS look for

Build a dedicated skills section organized by category: frameworks and libraries, cloud and MLOps platforms, languages, and data tooling. Paste keywords directly from job descriptions — terms like 'LLMOps,' 'RAG pipelines,' 'model quantization,' and 'distributed training' are now standard filters. Do not pad this section with generic tools like Microsoft Word.

Certifications that carry real weight with hiring teams include AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, the Deep Learning Specialization from Coursera (Andrew Ng), and MLOps Specialization from DeepLearning.AI. Databricks Certified Machine Learning Professional is increasingly requested at data-platform-heavy companies. List the issuing body and year earned.

• Frameworks: PyTorch, TensorFlow, JAX, Hugging Face Transformers, LangChain
• MLOps: MLflow, Kubeflow, SageMaker Pipelines, Vertex AI, Weights & Biases, DVC
• Languages: Python (primary), SQL, Bash; C++ or Rust a plus for inference optimization
• Data: Spark, dbt, Airflow, Kafka, Snowflake
• Certifications: AWS ML Specialty, Google ML Engineer, Databricks ML Professional

Education, licenses, and extras

List your highest degree first: institution, degree, field of study, and graduation year. A master's or PhD in computer science, statistics, or a related field is common in this role, but it is not a hard filter at most companies — a strong portfolio and measurable experience outweigh credentials at many employers. If your thesis or capstone involved model development, name it briefly.

Under a separate 'Publications & Projects' section, list any peer-reviewed papers with conference names (NeurIPS, ICML, ICLR carry weight), open-source contributions with star counts if notable, and Kaggle rankings if you placed in the top 5%. Keep this section concise — two to five items maximum. Do not list every side project you have ever touched.

• List degree, institution, and year — omit GPA unless it is above 3.7 and you graduated within the last three years
• Include thesis title if it is directly relevant to the roles you are targeting
• Publications: use short citation format with venue name and year
• Open-source: link to GitHub repo and note stars or forks if significant
• Kaggle or competition rankings: include only top-5% finishes

Mistakes to avoid

• ×Listing tools without context — writing 'PyTorch, TensorFlow, Kubernetes' in a skills section with no evidence of how you used them at scale tells a technical recruiter nothing actionable.
• ×Omitting model performance metrics and defaulting to vague language like 'improved model accuracy,' which signals you either did not measure results or do not know how to communicate them.
• ×Burying your GitHub or portfolio link in the body of the resume instead of the header, causing time-pressed reviewers to miss the work samples that differentiate you from other candidates.
• ×Using a single generic resume for every application instead of mirroring the specific terminology in each job description, which causes ATS keyword filters to rank your resume below less-experienced candidates who did tailor their submissions.

Frequently asked questions

Do I need a PhD to get hired as an AI Engineer?

No. A PhD is expected for pure research roles at labs like DeepMind or FAIR, but the majority of industry AI Engineer positions — including senior and staff levels — are filled by candidates with bachelor's or master's degrees who have strong portfolios and production experience. Demonstrable work on deployed systems consistently outweighs academic credentials in engineering-focused hiring.

How many pages should an AI Engineer resume be?

One page is standard for engineers with under ten years of experience. Two pages are acceptable — and sometimes expected — at the senior or staff level when you have publications, significant open-source contributions, or a long list of production systems to document. Never pad a one-page resume to two pages just to appear more experienced.

Should I include personal projects and Kaggle competitions on my AI Engineer resume?

Yes, if they demonstrate skills directly relevant to the roles you are targeting and if the results are noteworthy — a top-5% Kaggle finish or an open-source project with meaningful adoption are both worth including. Limit this section to two to five items and always link to the work. Listing a project without a link or measurable outcome adds clutter without adding credibility.