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7 AI Engineering Intern - Generative AI and RAG Resume Templates with Example

By Ria · Last Updated On October 24, 2026
Resume example — page 1

This guide shows you how to create an AI Engineering Intern - Generative AI and RAG resume that is clear, focused, and easy to review. Learn how to organize relevant evidence and present project work in a way that readers can follow.

Show how you prepared data, built or tested AI pipelines, and checked model results. For generative AI and RAG work, explain your use of Python, embeddings, vector search, grounding, evaluation, and responsible data practices where relevant.

The resume example is presented in 7 layouts, so you can compare the image previews and choose a format that suits your information. Open an example in the editor to view or customize it.

What Makes a Great AI Engineering Intern - Generative AI and RAG Resume in 2026

A strong resume uses clear sections and concise bullets to show relevant AI project work, technical skills, testing, and results. Keep the most useful evidence easy to find.

Use role-related terms naturally, such as RAG, Python, embeddings, vector search, model evaluation, grounding, and output validation. Support each term with a specific task or project detail.

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

#1 Simple One Column Format Resume Example

The Simple One Column Format keeps the resume in one vertical reading path, a practical choice for uninterrupted review.

 
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Why This Format Works

  • Experience and project entries follow one vertical path, keeping career evidence in sequence.
  • Reviewers can move between education, credentials, and skills without crossing columns.
  • The single reading path suits detailed technical sections with clear section breaks.

Best For

  • Linear section-by-section review
  • Project-heavy academic resumes
  • Text-focused resume presentation
  • A single reading path
  • Resumes with several content sections

Resume Keywords

  • engineering
  • Python
  • generative AI
  • machine learning
  • Developed a supervised RAG prototype
  • Used Hugging Face models and LLM APIs
  • Built a Python ML pipeline using structured public data, adding preprocessing
  • Feature engineering
  • model training
  • model evaluation
  • Prompt engineering
  • embeddings
  • Vector Search
  • LLM APIs
  • applied grounding strategies, guardrails, and output validation to flag unsupported responses and reduce hallucination risk
See all keywords
 

#2 Simple Two Column Format Resume Example

The Simple Two Column Format sets resume content in two columns, a practical choice when reviewers need distinct areas to navigate.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Two columns help distinguish experience and project details from supporting resume sections.
  • Skills, education, and credentials are easier to locate across distinct content areas.
  • Each content area can keep related entries together for focused section reviews.

Best For

  • Side-by-side information preference
  • Resumes with varied section types
  • Skills and credentials easy to locate
  • Experience paired with supporting details
  • Compact review of academic projects

#3 Plain One Column with Shaded Labels Resume Example

Plain One Column with Shaded Labels presents the resume in one vertical column, suited to readers who prefer a direct path through each section.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Project and experience sections remain within a single vertical reading path.
  • Reviewers can follow skills, education, and credentials without switching between columns.
  • A consistent section progression keeps technical details connected to their context.

Best For

  • One-column resume preference
  • Sequential review of project work
  • Technical details kept in context
  • Academic and project experience
  • A direct path through resume sections
 

#4 One Column Format with Photo Right Resume Example

One Column Format with Photo Right pairs a single text column with a photo on the right, for readers who want a portrait alongside the resume.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Experience and project entries stay in one text column for a steady review path.
  • Skills, education, and certifications remain part of the same body reading order.
  • One text column keeps resume sections from competing with a parallel content area.

Best For

  • Resume with a right-side photo
  • One-column text preference
  • Sequential review of experience
  • Project details in a single text flow
  • Portrait included with resume content
 

#5 Clean Two Column Sidebar Format Resume Example

Clean Two Column Sidebar Format places resume content in two columns, a useful choice for readers who want distinct areas beside one another.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Separate content areas help reviewers find experience, skills, and education efficiently.
  • The two-column reading structure keeps related resume sections easy to distinguish.
  • Project entries and supporting details can be reviewed as separate content groups.

Best For

  • Side-by-side resume sections
  • Quick access to supporting details
  • Distinct areas for varied content
  • Resume with several section types
  • Review of projects and credentials
 
 

#6 Two Column Format Resume Example

Two Column Format organizes the resume across two columns, a practical option when readers want to move between content areas.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Two columns give experience and education distinct areas within the overall resume.
  • Reviewers can locate skills and credentials without scanning one long vertical sequence.
  • Related project details can be grouped for easier section-by-section review.

Best For

  • Two-column resume organization
  • Experience and education in distinct areas
  • Skills and credentials easy to find
  • Section-by-section project review
  • Resumes with multiple information groups
 

#7 Photo Header Two Column Format Resume Example

Photo Header Two Column Format combines a photo in the header with a two-column resume body, suited to readers who want both features.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • The two-column body separates resume information into distinct areas for review.
  • Skills, education, and experience can be found without following one long column.
  • The header photo stays visually apart from the resume's section content.

Best For

  • Photo in the resume header
  • Two-column body preference
  • Resume with distinct content areas
  • Quick access to skills and education
  • Photo paired with detailed sections
 
 
 

Why Build Your AI Engineering Intern - Generative AI and RAG Resume with ResumeInMinutes

Compare resume layouts using image previews, then open an example in the editor to view or customize its structure. Keep your final content focused on relevant projects, skills, education, and technical evidence.

  • Clear resume section structure
  • Readable skills organization
  • Project-focused content
  • ATS-aware headings and keywords
  • Image previews for layout comparison
  • Editor access to view or customize examples

Choose a layout that makes your project evidence easy to scan. Use specific bullets to explain your contribution, methods, testing, and results without overstating the scale of your work.

 

See Also

 

Frequently Asked Questions

Are these AI Engineering Intern - Generative AI and RAG resume layouts ATS-friendly?

Use clear section headings, simple formatting, and relevant terms in context. For this role, useful topics may include Python, machine learning, RAG, embeddings, vector search, model evaluation, testing, and responsible AI.

Does this guide show several different resumes?

It presents one AI Engineering Intern - Generative AI and RAG resume example in different layouts. Compare the image previews and open an example in the editor to view or customize it.

What should an AI Engineering Intern - Generative AI and RAG resume include?

Include a focused summary, education, relevant experience or projects, technical skills, and certifications when applicable. Describe data preparation, model work, RAG methods, testing, and results with clear details.

How should I describe RAG project work?

Explain the data source, how you prepared text or embeddings, the retrieval method, and how you checked answer relevance or grounding. Include measurable results only when you have reliable evidence for them.

Can coursework and supervised projects support this resume?

Yes. Relevant coursework and project work can show practical skills. State your role, tools, methods, tests, and findings clearly, and describe the scope honestly.

Should I include a photo on my resume?

Include a photo only when it is expected in your country, region, or field. For U.S. applications, a resume without a photo is generally the safer choice.

ATS Score 96%

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 resume is used. This is not a match to a specific vacancy.
  • Text-based resume: 20%. The text resume assessed here receives 100 for this part.
  • Resume quality: 20%. We start at 100 and subtract points for issues found by our resume 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 resume 20% · Resume quality 20%

Keyword coverage: 40 of 43 (93%) · Resume quality: 100%

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

Details
  • ✓Name present: Josiah Singleton
  • ✓Email format: josiah.singleton@example.com
  • ✓Phone format: (312) 555-XXXX
  • ✓Experience 1: Job title: AI Engineering Research Assistant, Supervised Project
  • ✓Experience 1: Company name: University Research Lab
  • ✓Experience 1: Work location: Chicago, IL
  • ✓Experience 1: Employment dates: Jan 2026 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: AI Engineering Project Contributor
  • ✓Experience 2: Company name: Capstone Project
  • ✓Experience 2: Work location: Chicago, IL
  • ✓Experience 2: Employment dates: Sep 2025 - Dec 2025
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Applied AI and Automation Project
  • ✓Experience 3: Company name: Course Project
  • ✓Experience 3: Work location: Chicago, IL
  • ✓Experience 3: Employment dates: Jan 2025 - May 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 Resume 100% · Weight 20%

Parsed text
Josiah Singleton
(312) 555-XXXX
josiah.singleton@example.com
josiahsingleton
Chicago, IL 60614
SUMMARY
Computer Science student pursuing a bachelor's degree, with supervised academic project experience in machine learning and generative AI. Build and evaluate Python pipelines and RAG prototypes, documenting experiments and applying feedback to improve testing and grounded answers. Coursework and projects cover cloud deployment, model evaluation, responsible AI, and collaboration with technical and nontechnical reviewers.
EDUCATION
Bachelor of Science in Computer Science, Expected
2027
DePaul University
Chicago, IL
TECHNICAL SKILLS
Programming Languages: Python, SQL, Java
AI and Machine Learning Frameworks: PyTorch, TensorFlow, Hugging Face
Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform
Containers and Orchestration: Docker, Kubernetes, Amazon ECS
CI/CD and Version Control: GitHub Actions, Git, GitLab CI/CD
Vector Search and Data Stores: FAISS, Chroma, PostgreSQL
LLM and Data Libraries: Transformers, pandas, NumPy
APIs and Data Formats: REST, JSON, OpenAPI
Testing and Notebook Tools: pytest, Jupyter, scikit-learn
Microsoft Power Platform: Power Apps, Power Automate, Dataverse
SKILLS
Python
Data structures
Machine learning
Generative AI
RAG
Prompt engineering
Model evaluation
Feature engineering
AI testing
Technical documentation
Code review
Responsible AI
Teamwork
Analytical thinking
Technical communication
EXPERIENCE
AI Engineering Research Assistant, Supervised Project
Jan 2026 - Present
University Research Lab
Chicago, IL
Developed a supervised RAG prototype for searching 1,200 synthetic operations documents, preparing text chunks and embeddings and comparing vector search results to a keyword baseline for relevance and traceability.
Used Hugging Face models and LLM APIs to test model selection and capabilities, prompt engineering, instruction hierarchies, context windows, and context management; documented how knowledge cutoffs affected answers to time-sensitive questions.
Compared prompting, fine-tuning, and RAG approaches on a small labeled question set, then applied grounding strategies, guardrails, and output validation to flag unsupported responses and reduce hallucination risk.
Created repeatable evals and testing approaches for answer relevance, citation support, and failure cases; reviewed results with a faculty supervisor and presented tradeoffs in a concise technical briefing.
Applied security and PII awareness by using synthetic data, limiting input fields, and documenting responsible AI and governance considerations for potential enterprise use cases.
Presented prototype changes in supervised code reviews, incorporated faculty feedback into Python retrieval and validation components, and documented revisions for repeatable evaluation by the research team.
AI Engineering Project Contributor
Sep 2025 - Dec 2025
Capstone Project
Chicago, IL
Built a Python ML pipeline using structured public data, adding preprocessing, exploratory dataset analysis, feature generation, model training, and test workflows to compare baseline classifiers.
Packaged the prototype in Docker and deployed a demonstration to AWS; used Kubernetes manifests in a local learning environment to understand container orchestration and deployment configuration.
Added GitHub Actions CI/CD checks for formatting and automated tests, and documented how distributed computing concepts could support larger data workloads without overstating the prototype's scale.
Wrote test cases and validation procedures for input handling and model reliability, then presented limitations, findings, and next steps to classmates and a faculty reviewer.
Applied AI and Automation Project
Jan 2025 - May 2025
Course Project
Chicago, IL
Analyzed a de-identified public service dataset with Python and foundational data structures, prepared features, and evaluated introductory AI/ML models using separate training and inference workflows.
Prototyped an approval workflow with Microsoft Power Apps and Power Automate, documenting data checks and human review points for a low-code automation scenario.
Tested Replit Agent for a small developer task, inspected generated code for correctness, and recorded where agentic workflows and tool use required careful human verification.
Completed course labs comparing PyTorch and TensorFlow examples, and submitted clear experiment notes that incorporated instructor feedback after debugging inconsistent results.
LEADERSHIP, AWARDS & RECOGNITION
Computer Science Department Project Showcase, Faculty Commendation for Clear Technical Presentation, 2025.
Dean's List, DePaul University, 2024-2025.
Facilitated a Computer Science Student Association peer study group during 2025-2026, helping classmates work through course material.
Volunteered as a tutor with the DePaul University Peer Learning Program in 2025, assisting fellow students with coursework.
CERTIFICATIONS
Microsoft Certified: Azure AI Fundamentals (AI-900)
2026
AWS Academy Cloud Foundations
2025
PROFESSIONAL AFFILIATIONS
Student member, Association for Computing Machinery (ACM), engaging with computing education and technical community resources.
Student member, Institute of Electrical and Electronics Engineers (IEEE), participating in a professional community focused on computing and engineering.
LANGUAGES
English (Native)
Spanish (Intermediate)

Matched keywords (40)

  • engineeringOriginal keyword in JD: Currently pursuing a degree in Computer Science OR Engineering OR Data Science OR a related discipline
  • Python
  • generative AI
  • machine learning
  • Developed a supervised RAG prototypeOriginal keyword in JD: Retrieval-augmented generation (RAG)
  • Used Hugging Face models and LLM APIsOriginal keyword in JD: Large language models (LLMs)
  • Built a Python ML pipeline using structured public data, adding preprocessingOriginal keyword in JD: Data preprocessing
  • Feature engineering
  • model training
  • model evaluation
  • Prompt engineering
  • embeddings
  • Vector Search
  • LLM APIs
  • applied grounding strategies, guardrails, and output validation to flag unsupported responses and reduce hallucination riskOriginal keyword in JD: Grounding and hallucination reduction
  • guardrails, and output validation
  • Wrote test cases and validation procedures for input handling and model reliabilityOriginal keyword in JD: AI testing and validation
  • Hugging FaceOriginal keyword in JD: PyTorch OR TensorFlow OR Hugging Face
  • AWSOriginal keyword in JD: AWS OR Azure OR GCP
  • Docker
  • Kubernetes
  • CI/CD
  • documented how distributed computing concepts could support larger data workloadsOriginal keyword in JD: Distributed computing fundamentals
  • Data structures
  • fine-tuning
  • training and inference
  • model selection and capabilities
  • instruction hierarchies
  • context windows, and context management
  • knowledge cutoffs
  • agentic workflows and tool use
  • responsible AI and governance
  • security and PII awareness
  • Built a Python ML pipelineOriginal keyword in JD: AI pipeline development
  • Technical documentation
  • Code review
  • Analytical thinking
  • Teamwork
  • Microsoft Power AppsOriginal keyword in JD: Microsoft Power Apps OR Power Automate
  • Replit Agent

Missing keywords (3)

  • Vector databases
  • Cost and performance considerations
  • Technical and nontechnical communication
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.

Resume 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 organization.

Content Accuracy and Verification: To the fullest extent permitted by applicable law, we do not represent or warrant that resume 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 resume 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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