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7 Trainee AI Engineer - Generative AI and Machine Learning CV Templates with Example

By Ria · Last Updated On 19 October 2026
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This guide shows you how to create a Trainee AI Engineer - Generative AI and Machine Learning CV that is clear, focused and easy to review. You will learn how to present your project evidence and choose a layout that supports a quick scan.

For this role, a useful CV connects AI training with practical work in machine learning and generative AI. Clear bullets can explain how you prepared data, tested model outputs and considered privacy, bias and fairness.

Compare the example in 7 layouts using the PNG image previews, then open one in the editor to customise it. The guide also covers ATS-friendly structure and role terms such as Python, scikit-learn, Hugging Face, prompt engineering, retrieval-augmented generation (RAG) and vector databases.

What Makes a Great Trainee AI Engineer - Generative AI and Machine Learning CV in 2026

A strong CV makes project work, training and technical skills easy to find. Use concise bullets to show how you prepared data, tested models and documented results.

Include relevant terms such as Python, machine learning, large language models, RAG, prompt engineering and AI ethics. Support claims with project details, and add figures only when you can verify them.

Note: CV examples are for educational purposes only. Use only information that accurately reflects your experience and qualifications. References to real organisations do not imply affiliation or endorsement.

#1 Simple One Column Format CV Example

Simple One Column Format keeps the CV body in one vertical reading path, making a straightforward review easier.

 
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Why This Format Works

  • A single reading path helps reviewers follow project experience, education and credentials in sequence.
  • Consistent section order keeps training, certifications and technical skills easy to locate.
  • Full-width text gives project bullets room to explain testing, limits and findings.

Best For

  • Single-path section review
  • Detailed project records
  • Training alongside formal education
  • Several certification entries
  • Text-led CV submissions

CV Keywords

  • Python programming
  • machine learning
  • generative AI
  • neural networks
  • large language models
  • object-oriented programming
  • retrieval-augmented generation
  • prompt engineering
  • Created a customer service chatbot prototype
  • Vector Databases
  • Knowledge bases
  • scikit-learn
  • Hugging Face
  • Built a sentiment analysis classifier
  • Streamlit
See all keywords
 

#2 Simple Two Column Format CV Example

Simple Two Column Format divides the CV body into two columns, giving readers a compact way to review grouped sections.

 
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Why This Format Works

  • Two columns distinguish information groups while keeping project experience easy to follow.
  • Separate column areas can make skills and education easier to locate.
  • Project bullets retain their place in the wider career and training record.

Best For

  • Compact section grouping
  • Side-by-side information review
  • CVs with several distinct sections
  • Project details beside supporting information
  • Readers comparing section groups

#3 Plain One Column with Shaded Labels CV Example

Plain One Column with Shaded Labels presents the CV in one continuous column, suiting readers who prefer a direct sequence through sections.

 
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Why This Format Works

  • One continuous column keeps project evidence in a clear top-to-bottom order.
  • Section breaks help reviewers move between education, training and certification details.
  • Room for full project bullets preserves the context behind testing and evaluation.

Best For

  • Direct section-by-section reading
  • Full project descriptions
  • Training and education records
  • Clear separation between CV sections
  • Text-focused document review
 

#4 One Column Format with Photo Right CV Example

One Column Format with Photo Right places a portrait to the right of the CV, adding a visual identifier while keeping the content in one reading path.

 
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Why This Format Works

  • A single-column sequence keeps project experience, education and skills in a familiar order.
  • The portrait adds a personal visual element without dividing the written sections.
  • Project bullets can present testing and evaluation details in their original context.

Best For

  • CVs that include a portrait
  • Single-path section review
  • Detailed project descriptions
  • Education and training records
  • A personal visual element
 

#5 Clean Two Column Sidebar Format CV Example

Clean Two Column Sidebar Format uses two columns to group CV information, a useful choice for readers who want a compact view of distinct sections.

 
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Why This Format Works

  • Two columns give separate section groups room while keeping the overall reading order clear.
  • Skills and education can be found without moving through every project bullet.
  • The layout keeps project details connected to the wider training and experience record.

Best For

  • Compact CV section groups
  • Readers seeking quick section access
  • Several kinds of supporting information
  • Skills and education overview
  • Project details within a wider record
 
 

#6 Two Column Format CV Example

Two Column Format sets CV information across two columns, giving readers a compact view of grouped sections and project content.

 
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Why This Format Works

  • The two-column structure gives section groups a clear visual distinction.
  • Project experience remains easy to follow alongside education and certification information.
  • A compact arrangement helps reviewers locate skills across a multi-section CV.

Best For

  • Compact multi-section CVs
  • Grouped project and training information
  • Readers locating skills quickly
  • Several education or certification entries
  • Side-by-side section review
 

#7 Photo Header Two Column Format CV Example

Photo Header Two Column Format pairs a portrait with a two-column CV body, offering a visual identifier alongside grouped information.

 
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Why This Format Works

  • Two columns distinguish section groups while keeping the CV's reading order clear.
  • The portrait adds a visual point of identification without serving as evidence of qualifications.
  • Project and training details remain part of the same organised CV.

Best For

  • CVs including a portrait
  • Grouped information in two columns
  • Project and training records
  • Readers reviewing distinct CV sections
  • A visual point of identification
 
 
 

Why Build Your Trainee AI Engineer - Generative AI and Machine Learning CV with ResumeInMinutes

ResumeInMinutes helps you organise AI project work, technical skills and training in a clear CV. Compare PNG image previews, then open an example in the editor to customise its content and layout.

  • Different CV layouts
  • Clear, readable section structure
  • Space for role-specific keywords
  • Organised skills and project evidence
  • PNG image previews
  • CV customisation in the editor

Choose a format that makes your AI training and project work easy to follow. Keep the focus on data preparation, model testing, responsible AI and the tools you have used.

 

See Also

 

Frequently Asked Questions

Are these Trainee AI Engineer - Generative AI and Machine Learning CV templates ATS-friendly?

The examples use readable headings and clear sections to support scanning. Add relevant terms naturally, such as Python, machine learning, generative AI, RAG, scikit-learn and prompt engineering.

Are these separate CV examples or different layouts of one example?

This page presents one role-focused CV example in different layouts. Compare the PNG image previews, then open an example in the editor to customise it.

What should a Trainee AI Engineer - Generative AI and Machine Learning CV include?

Include a focused summary, relevant training, project experience, education, skills and certifications. Explain your work with data preparation, model evaluation, machine learning, generative AI and responsible AI practices.

Which CV layout works well for this role?

Choose a layout with clear headings and an easy-to-follow order for skills, projects, training and education. A simple structure can help recruiters and applicant tracking systems find key details.

How can I show AI project achievements on my CV?

Use concise bullet points to explain the task, your methods and what you tested or learned. For example, describe how you prepared a dataset, compared model outputs or reviewed chatbot retrieval gaps. Include measurable results only when you can support them.

Should I include a photo on my CV?

For UK applications, a photo is usually not needed. Use the space for relevant project work, skills and training instead.

ATS score and CV checks

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ATS Score 94%

How we calculate this score

  • Keywords: 60% of the total. AI extracts keywords from your job description. If you do not provide one, a standard job description for the title matching your CV is used. This is not a match to a specific vacancy.
  • Text-based CV: 20%. The text CV assessed here receives 100 for this part.
  • CV quality: 20%. We start at 100 and subtract points for issues found by our CV checks, down to a minimum of zero.
  • Keyword coverage = round(100 × confirmed matches ÷ total keywords). Exact matches and supported equivalents verified by AI receive full credit; unsupported keywords receive none. Live text checks use the saved equivalents and do not request a new AI review. Without keywords, no overall ATS score is available.
  • Final score = 60% of keyword coverage + 20% of the text score + 20% of the quality score, rounded to a whole number. Without keywords, no overall ATS score is available.

What this score means

Our scores are estimates, not employer ratings. They may contain errors and do not guarantee accurate parsing, a ranking, an interview, or a job. Check your document against the employer’s requirements. You are responsible for relying on this score.

Terms and Conditions

Score weights: Keywords 60% · Text based CV 20% · CV quality 20%

Keyword coverage: 26 of 29 (90%) · CV quality: 100%

CV quality 100% · Weight 20% · Issues (0)

Details
  • ✓Name present: Isla Ahmed
  • ✓Email format: isla.ahmed@example.com
  • ✓Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: Trainee AI Engineer, RAG Customer Service Chatbot
  • ✓Experience 1: Company name: Capstone Project
  • ✓Experience 1: Work location: Remote
  • ✓Experience 1: Employment dates: Jan 2026 - Oct 2026
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Trainee AI Engineer, Sentiment Analysis
  • ✓Experience 2: Company name: Academic Research
  • ✓Experience 2: Work location: Remote
  • ✓Experience 2: Employment dates: Sep 2025 - Dec 2025
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Trainee AI Engineer, Machine Learning and Streamlit
  • ✓Experience 3: Company name: University Project
  • ✓Experience 3: Work location: Coventry, West Midlands
  • ✓Experience 3: Employment dates: Jun 2025 - Aug 2025
  • ✓Experience 3: Description or achievements: Present
  • ✓No placeholder fields
  • ✓No repeated achievement bullets
  • ✓No empty bullets

No issues found by the current checks.

Text Based CV 100% · Weight 20%

Parsed text
Isla Ahmed
07700 90XXXX
isla.ahmed@example.com
islaahmed
Coventry, West Midlands, CV3 4FJ
SUMMARY
Career-transition candidate developing AI engineering capability through applied machine learning and generative AI projects. Build Python prototypes for car price prediction, sentiment classification and a retrieval-augmented customer service chatbot, preparing data and evaluating model outputs to understand their strengths and limits. Use prompt engineering and a vector database to ground chatbot responses in a knowledge base, with attention to privacy, bias and fairness. Training in neural networks, large language models and object-oriented programming is supported by Microsoft AI-900 certification, AWS Cloud Practitioner study and project presentations.
EDUCATION
BSc Psychology
2020
Coventry University
Coventry, UK
AI Engineering and Machine Learning Training Programme
2026
Northstar Digital Academy
Online, self-paced
Coursework: AI engineering fundamentals, neural networks, large language models, data workflows, prompt engineering, retrieval-augmented generation, machine learning and AI ethics.
TECHNICAL SKILLS
Programming Languages: Python, SQL, JavaScript
Machine Learning Libraries: scikit-learn, XGBoost, LightGBM
Data Analysis Libraries: NumPy, Pandas, SciPy
Visualisation Tools: Matplotlib, Seaborn, Plotly
AI and NLP Libraries: Hugging Face Transformers, NLTK, spaCy
Notebook Environments: Jupyter Notebook, Google Colab, JupyterLab
Development Environments: Visual Studio Code, PyCharm, GitHub Codespaces
App and Web Frameworks: Streamlit, Flask, FastAPI
Vector Databases: Chroma, FAISS, Pinecone
Cloud Platforms: Amazon Web Services, Microsoft Azure, Google Cloud Platform
Version Control: Git, GitHub, GitLab
Data Formats: CSV, JSON, YAML
SKILLS
Python programming
Machine learning
Generative AI
Neural networks
Large language models
Object-oriented programming
Data preparation
Model evaluation
Prompt engineering
RAG
Vector databases
Knowledge bases
Sentiment analysis
Data visualisation
AI ethics
EXPERIENCE
Trainee AI Engineer, RAG Customer Service Chatbot
Jan 2026 - Oct 2026
Capstone Project
Remote
Created a customer service chatbot prototype that retrieved relevant answers from a curated knowledge base, using Python, prompt engineering and retrieval-augmented generation (RAG) to ground responses in source material.
Prepared and organised source documents for retrieval, then connected a vector database to support semantic search and context selection for incoming questions.
Tested representative customer enquiries against expected answers, recording retrieval gaps and refining prompts and source content to make responses more relevant and traceable.
Documented data privacy considerations and potential bias in the prototype, and presented its design choices and limitations during a virtual oral examination.
Trainee AI Engineer, Sentiment Analysis
Sep 2025 - Dec 2025
Academic Research
Remote
Built a sentiment analysis classifier with Hugging Face using a labelled text dataset, reviewing examples and preparing the data before model evaluation.
Compared classifier outputs with labelled examples to identify misclassifications and explain how dataset composition could affect fairness and model behaviour.
Presented the model workflow, evaluation findings and responsible-use considerations in a project report, applying AI ethics principles including bias and data privacy.
Trainee AI Engineer, Machine Learning and Streamlit
Jun 2025 - Aug 2025
University Project
Coventry, West Midlands
Developed a Streamlit car price prediction app in Python, turning a cleaned vehicle dataset into an interactive demonstration of a machine learning workflow.
Used Pandas and NumPy to inspect, clean and prepare records, then applied Matplotlib to visualise feature patterns before training and testing regression models with scikit-learn.
Compared machine learning algorithms using held-out test data and documented the evaluation approach, limitations and results for a course presentation.
Used Jupyter Notebooks and VS Code to practise Python fundamentals and object-oriented programming (OOP), keeping code and project notes organised for review.
LEADERSHIP, AWARDS & RECOGNITION
Received commendation in the training programme’s 2026 final project review for clear project documentation and discussion of responsible AI.
Organised an informal peer study group for Python and machine learning learners in 2025–2026, sharing revision notes and facilitating weekly practice discussions.
CERTIFICATIONS
Microsoft Certified: Azure AI Fundamentals (AI-900)
2026
AWS Certified Cloud Practitioner
2026
UK-accredited AI engineering programme qualification
2026
PROFESSIONAL AFFILIATIONS
Member, British Computer Society (BCS), engaging with a professional community focused on computing and technology.
Member, Association for Computing Machinery (ACM), participating in an organised community for computing professionals and learners.

Matched keywords (26)

  • Python programming
  • machine learning
  • generative AI
  • neural networks
  • large language modelsOriginal keyword in JD: Large language models (LLMs)
  • object-oriented programmingOriginal keyword in JD: Object-oriented programming (OOP)
  • retrieval-augmented generation
  • prompt engineering
  • Created a customer service chatbot prototypeOriginal keyword in JD: AI customer service chatbot development
  • Vector Databases
  • Knowledge bases
  • scikit-learn
  • Hugging Face
  • Built a sentiment analysis classifierOriginal keyword in JD: Sentiment analysis classification
  • Streamlit
  • car price prediction
  • Jupyter Notebooks
  • VS Code
  • NumPy
  • Pandas
  • Matplotlib
  • Data visualisation
  • AI ethics
  • data privacy
  • Microsoft AI-900 certification
  • AWS Certified Cloud Practitioner

Missing keywords (3)

  • AI bias and fairness
  • AI data collection, cleaning and preparation
  • Model training, testing and evaluation
Disclaimer: All names, images, contact information, employment histories, achievements, security clearances, military credentials, and other qualifications shown in this content are fictional and used for illustrative purposes only. Any resemblance to actual persons, living or deceased, is purely coincidental.

CV and cover letter examples and recommendations in this article reflect general best practices and professional experience and are provided for guidance only. Results may vary based on role, industry, region, and hiring organisation.

Content Accuracy and Verification: To the fullest extent permitted by applicable law, we do not represent or warrant that CV or cover letter guidance, examples, recommendations, or related information are accurate, complete, current, error-free, or suitable for any particular purpose. You are responsible for independently reviewing and verifying the information before relying on it.

ATS and Cover Letter Scores: ATS scores are estimates calculated using our own formula, informed by publicly available guidance on CV formatting and keyword matching. They are not scores issued or endorsed by an ATS provider or employer. Cover letter scores are also our own assessments. Parsing, weighting, screening criteria, and results vary between systems, employers, job descriptions, and document formats. No score guarantees successful parsing, a particular ranking, an interview, or employment. Use scores as general guidance and independently check your documents against the employer’s requirements. To the fullest extent permitted by applicable law, we accept no liability for errors in scores or parsing, or losses arising from reliance on these assessments. Nothing in this notice excludes liability or limits consumer rights that cannot lawfully be excluded or limited.

Terms and Conditions
 
 
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