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7 AI Engineer Apprentice - Generative AI CV Templates with Example

By Ria · Last Updated On 22 October 2026
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This guide shows you how to create an AI Engineer Apprentice - Generative AI CV that is clear, focused and easy to review. It explains how to present relevant evidence and choose a layout that supports a quick scan.

A strong CV for this role shows how you prepare data, test models and build useful AI tools. Use clear bullet points to connect technical work with user needs, model results and responsible AI checks.

Compare the example presented in 7 layouts using the PNG image previews, then open a preferred version in the editor to customise it. The guide also covers ATS readability and relevant terms such as Python, large language models, prompt engineering, APIs, Microsoft Azure and data drift.

What Makes a Great AI Engineer Apprentice - Generative AI CV in 2026

A strong CV makes data preparation, model testing and AI project work easy to scan. Show what you built, how you checked it and what the work helped users do.

Use focused bullets and relevant terms, such as Python, generative AI, APIs, model evaluation and responsible AI. Include results only when you can support 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

The Simple One Column Format presents the CV in one reading path, making its section order easy to follow from top to bottom.

 
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Why This Format Works

  • One reading path lets reviewers follow experience, education and credentials in sequence.
  • Shared column width gives skills and project details consistent room.
  • Plain section progression keeps career history easy to scan from top to bottom.

Best For

  • Single-column reading preference
  • Straightforward section navigation
  • Chronological experience reviews
  • Detailed project descriptions
  • Education-led CV structure

CV Keywords

  • Python
  • generative AI
  • AI and machine learning
  • Prepared, cleaned and explored synthetic housing repair records with pandas and NumPy
  • AI model development
  • Large language models
  • Prompt engineering
  • API development and integration
  • Vector Databases
  • the LangChain orchestration framework
  • Packaged a selected model as a prototype service on Microsoft Azure and simulated changing input patterns to track performance and data drift, rather than presenting the exercise as a live production deployment.
  • using held-out data and suitable evaluation measures to explain model strengths and limitations.
  • Data drift monitoring
  • Responsible AI
  • Bias assessment
See all keywords
 

#2 Simple Two Column Format CV Example

The Simple Two Column Format gives CV content two parallel areas, helping reviewers take in career details and supporting sections together.

 
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Why This Format Works

  • Two content areas let reviewers move between experience and supporting sections with less page-wide scanning.
  • Separate columns give skills and education visible spaces in the overall structure.
  • The parallel layout suits project evidence alongside a clear career history.

Best For

  • Parallel content review
  • Several supporting sections
  • Experience with project evidence
  • Skills and education visibility
  • Structured recruiter scanning

#3 Plain One Column with Shaded Labels CV Example

Plain One Column with Shaded Labels uses shaded section labels in a single-column CV, giving reviewers clear visual markers as they read.

 
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Why This Format Works

  • Shaded labels make education, skills and experience headings easier to spot during a quick review.
  • Single-column reading order keeps project evidence connected to its surrounding details.
  • Consistent section markers help reviewers return to credentials and achievements without searching across columns.

Best For

  • Label-led section navigation
  • Single-column reading preference
  • Quick section look-ups
  • Project details with context
  • Clearly grouped credentials
 

#4 One Column Format with Photo Right CV Example

One Column Format with Photo Right places a photo on the right and keeps the CV content in a single-column reading order.

 
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Why This Format Works

  • One-column body content keeps experience, education and skills in a familiar top-to-bottom sequence.
  • Full-width sections give project details room to retain context across bullet points.
  • The right-side photo adds a visual element without splitting the main content into columns.

Best For

  • Right-side photo preference
  • Single-column reading order
  • Full-width project descriptions
  • Sequential experience review
  • CVs with a visual header
 

#5 Clean Two Column Sidebar Format CV Example

Clean Two Column Sidebar Format gives the sidebar and main content separate areas, placing supporting details alongside longer career entries.

 
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Why This Format Works

  • The sidebar gives supporting information its own area beside the main career content.
  • A separate main column leaves room for detailed experience and project entries.
  • Two distinct areas help reviewers locate different kinds of CV content.

Best For

  • Sidebar-based information grouping
  • Detailed experience entries
  • Supporting details beside career history
  • Multiple CV sections
  • Two-area reading preference
 
 

#6 Two Column Format CV Example

Two Column Format arranges the CV in parallel content areas, offering a direct way to review separate sections side by side.

 
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Why This Format Works

  • Two columns let reviewers compare separate CV sections without following one long reading path.
  • Distinct content areas make skills, education and credentials easier to locate.
  • Parallel reading paths suit CVs with several types of supporting detail.

Best For

  • Side-by-side section review
  • Several supporting details
  • Skills and education overview
  • Separate experience and credentials
  • Parallel reading preference
 

#7 Photo Header Two Column Format CV Example

Photo Header Two Column Format places a photo in the header above a CV arranged in two content columns.

 
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Why This Format Works

  • Two content columns give career history and supporting sections separate routes through the CV.
  • A header photo provides a visible identity cue before reviewers reach the CV content.
  • Parallel areas keep project, education and skills sections accessible during scanning.

Best For

  • Photo-led header preference
  • Two-column content review
  • Career history with supporting sections
  • Projects alongside education
  • Visible identity in the header
 
 
 

Why Build Your AI Engineer Apprentice - Generative AI CV with ResumeInMinutes

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

  • Clear CV layouts
  • Readable section structure
  • Space for relevant AI keywords
  • Organised skills and project evidence
  • PNG image previews
  • CV customisation in the editor

Choose a format that makes your technical evidence easy to follow. Keep the focus on model development, testing, user needs and responsible use of AI.

 

See Also

 

Frequently Asked Questions

Are these AI Engineer Apprentice - Generative AI CV templates ATS-friendly?

The examples use clear headings and sections to support scanning. Add relevant terms naturally, such as Python, generative AI, prompt engineering, APIs, model testing and data drift.

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

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

What should an AI Engineer Apprentice - Generative AI CV include?

Include a focused summary, relevant projects or experience, education, skills and certifications. Give clear evidence of data preparation, Python development, model evaluation, API work and responsible AI checks where relevant.

Which CV layout works well for this role?

Choose a layout with clear headings and an easy-to-follow order for skills, experience and education. A simple structure helps readers find technical evidence quickly.

How can I show AI project results on my CV?

Write concise bullet points that explain the problem, your contribution and how you tested the result. Include supported measures, such as evaluation findings or response quality, and state when work was a prototype.

Should I include a photo on my CV?

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

ATS score and CV checks

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ATS Score 93%

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: 33 of 37 (89%) · CV quality: 100%

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

Details
  • ✓Name present: Callum Hall
  • ✓Email format: callum.hall@example.com
  • ✓Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: AI Engineer, Generative AI Capstone Project
  • ✓Experience 1: Company name: University Project
  • ✓Experience 1: Work location: Leeds, UK
  • ✓Experience 1: Employment dates: Jan 2026 - May 2026
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Machine Learning Researcher, University Research Project
  • ✓Experience 2: Company name: Academic Research
  • ✓Experience 2: Work location: Remote
  • ✓Experience 2: Employment dates: Feb 2025 - Nov 2025
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: AI Engineer, Speech and Image Processing Course Project
  • ✓Experience 3: Company name: University Project
  • ✓Experience 3: Work location: Leeds, UK
  • ✓Experience 3: Employment dates: Sep 2024 - Dec 2024
  • ✓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
Callum Hall
07700 90XXXX
callum.hall@example.com
callumhall
Leeds, LS6 2AB
SUMMARY
Recent BSc Mathematics graduate focused on AI and machine learning, with university experience developing Python models and generative AI prototypes. Built tools for housing and social care scenarios, testing model outputs, documenting limitations and considering bias, privacy and governance. Collaborated with student product and user experience groups to set success criteria and make AI interfaces clearer and easier to use. Communicate technical findings to non-technical audiences, drawing on sound mathematical knowledge and practical experience with data preparation, APIs and model evaluation.
EDUCATION
BSc Mathematics
2026
University of Leeds
Leeds, UK
A-levels: Mathematics (B), Physics (B) and Computing (B)
2022
Northfield Sixth Form
UK
TECHNICAL SKILLS
Programming Languages: Python, SQL, JavaScript
Data Analysis Libraries: pandas, NumPy, SciPy
Machine Learning Frameworks: scikit-learn, PyTorch, TensorFlow
Cloud Platforms: Microsoft Azure, Azure Machine Learning, Azure App Service
API and Web Frameworks: FastAPI, Flask, Django
Generative AI Tools: LangChain, Hugging Face Transformers, OpenAI API
Vector Databases: Chroma, FAISS, Qdrant
Development Tools: Git, Jupyter Notebook, Visual Studio Code
Data Formats and Query: JSON, CSV, SQLite
Testing and Serving: pytest, Uvicorn, Docker
SKILLS
Python
AI and machine learning
Data preparation and cleaning
AI model development
Generative AI
Large language models
Prompt engineering
API development and integration
Model testing and evaluation
Responsible AI
Bias assessment
Data drift monitoring
Technical documentation
Statistics and algebra
Stakeholder collaboration
EXPERIENCE
AI Engineer, Generative AI Capstone Project
Jan 2026 - May 2026
University Project
Leeds, UK
Built a question-answering prototype for housing and social care guidance using Python, an LLM API, prompt engineering, a vector database and the LangChain orchestration framework, grounding responses in a curated set of public documents.
Developed FastAPI endpoints for integration with a sample service interface, then tested model responses against agreed success criteria with student product and user experience groups to improve clarity and ease of use.
Deployed a demonstration service to a Microsoft Azure test environment; reviewed response quality and performance, recorded limitations and responsible AI checks, and wrote setup and support documentation for reuse.
Machine Learning Researcher, University Research Project
Feb 2025 - Nov 2025
Academic Research
Remote
Prepared, cleaned and explored synthetic housing repair records with pandas and NumPy, documenting data quality decisions before modelling recurring repair demand and prioritisation patterns.
Compared algorithms with scikit-learn and course exercises in PyTorch and TensorFlow, using held-out data and suitable evaluation measures to explain model strengths and limitations.
Packaged a selected model as a prototype service on Microsoft Azure and simulated changing input patterns to track performance and data drift, rather than presenting the exercise as a live production deployment.
Assessed potential bias and personal-data risks, applied security and governance checks to the synthetic data workflow, and presented findings in plain language to non-technical reviewers.
AI Engineer, Speech and Image Processing Course Project
Sep 2024 - Dec 2024
University Project
Leeds, UK
Created Python demonstrations for speech-to-text and image processing, using a PyTorch-based transcription workflow and TensorFlow image classification to explore different AI applications.
Used Jupyter Notebooks and Git to organise experiments, evaluate transcription quality and image-classification results, and record reproducible testing steps for the course presentation.
LEADERSHIP, AWARDS & RECOGNITION
Commended at the University of Leeds Applied Mathematics Project Showcase in 2026 for clearly explaining model evaluation and limitations.
Received departmental project presentation recognition in 2025 for communicating a data-modelling study to a non-specialist audience.
Mentored first-year quantitative methods students from 2025 to 2026, sharing study resources and supporting small-group problem-solving sessions.
Volunteered with the campus AI and Society forum from 2024 to 2025, helping organise accessible demonstrations and discussions about responsible AI.
CERTIFICATIONS
Microsoft Certified: Azure AI Fundamentals (AI-900)
2025
Responsible AI Fundamentals, IBM SkillsBuild
2025
PROFESSIONAL AFFILIATIONS
Member, BCS, The Chartered Institute for IT, engaging with professional resources relevant to computing and artificial intelligence.
Member, The Alan Turing Institute community, following research and discussion on data science and responsible AI.
LANGUAGES
English (Native)
French (Intermediate)

Matched keywords (33)

  • Python
  • generative AI
  • AI and machine learning
  • Prepared, cleaned and explored synthetic housing repair records with pandas and NumPyOriginal keyword in JD: Data preparation, cleaning and exploration
  • AI model development
  • Large language models
  • Prompt engineering
  • API development and integration
  • Vector Databases
  • the LangChain orchestration frameworkOriginal keyword in JD: Orchestration frameworks
  • Packaged a selected model as a prototype service on Microsoft Azure and simulated changing input patterns to track performance and data drift, rather than presenting the exercise as a live production deployment.Original keyword in JD: Model deployment and monitoring
  • using held-out data and suitable evaluation measures to explain model strengths and limitations.Original keyword in JD: Model performance evaluation
  • Data drift monitoring
  • Responsible AI
  • Bias assessment
  • Assessed potential bias and personal-data risks, applied security and governance checks to the synthetic data workflowOriginal keyword in JD: Personal-data protection
  • security and governance
  • Technical documentation
  • Statistics and algebraOriginal keyword in JD: Statistics and basic algebra
  • Communicate technical findings to non-technical audiencesOriginal keyword in JD: Explaining technical concepts to non-technical audiences
  • tested model responses against agreed success criteria with student product and user experience groupsOriginal keyword in JD: Stakeholder and product collaboration
  • tested model responses against agreed success criteriaOriginal keyword in JD: Success criteria and model testing
  • pandas
  • NumPy
  • scikit-learn
  • PyTorch
  • TensorFlow
  • Microsoft Azure
  • Git
  • Jupyter Notebooks
  • speech-to-text
  • Image Processing
  • BSc MathematicsOriginal keyword in JD: Two relevant A-levels and B in A-level Mathematics OR two relevant A-levels and two years of technology work experience OR Level 4 or Level 5 apprenticeship in a similar technical subject OR International Baccalaureate at Level 3 in a relevant subject OR at least three years in a related role OR first degree in a numerate discipline excluding Computer Science

Missing keywords (4)

  • Logical problem-solving
  • Written and spoken communication
  • Accessible AI tools
  • Public services, housing and social care
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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