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7 AI Engineering Intern - Agentic 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 - Agentic AI and RAG resume that presents your skills and project work clearly. You will learn how to organize evidence for quick review and compare professional resume layouts.

For this role, show how you build and test AI agents and retrieval-augmented generation pipelines. Clear bullets can explain data ingestion, embeddings, retrieval, prompt testing, and how you evaluate response quality.

Explore one AI Engineering Intern - Agentic AI and RAG resume example presented in 7 resume layouts. The image previews help you compare designs, and you can open an example in the editor to customize it.

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

A strong resume uses clear sections and concise bullets to show AI project work, testing, collaboration, and technical communication. Put relevant skills where reviewers can find them quickly.

Use accurate keywords such as AWS Bedrock, Strands, Model Context Protocol (MCP), LLMs, RAG, REST APIs, embeddings, and prompt engineering. Support them with specific project evidence and evaluation methods.

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

A single-column layout keeps the resume on one direct reading path, a good choice for readers who prefer a straightforward review.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • One reading path lets reviewers follow experience, projects, education, and credentials in order.
  • Full-width sections give project results and supporting details room to remain readable.
  • A consistent section sequence helps reviewers locate skills without navigating parallel content.

Best For

  • Sequential section review
  • Long project descriptions
  • Chronological experience records
  • Text-based resume handling
  • Education and project details

Resume Keywords

  • AI agents
  • agentic workflows
  • retrieval-augmented generation
  • AWS Bedrock
  • Strands
  • MCP
  • LLMs
  • AI-powered web applications
  • AI productivity tool
  • REST APIs
  • used REST APIs to combine course scheduling data with a third-party calendar service
  • Connected the prototype to REST APIs and simulated business systems
  • ingests course and policy documents
  • embeddings
  • Information retrieval
See all keywords
 

#2 Simple Two Column Format Resume Example

Two columns place resume content in parallel areas, a practical choice when readers want distinct sections visible together.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Side-by-side sections help reviewers compare project work with education and supporting details.
  • Separate reading areas keep skills and credentials distinct from experience summaries.
  • Parallel content areas make project history easier to scan without losing section order.

Best For

  • Parallel content groups
  • Several project entries
  • Cross-section comparison
  • Skills and credential summaries
  • Academic project records

#3 Plain One Column with Shaded Labels Resume Example

This one-column layout keeps sections in a continuous sequence, helping readers move through project, education, and skills content naturally.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • A single reading path keeps experience, projects, and education in sequence.
  • Clear section breaks help reviewers move from project results to credentials.
  • Full-width content gives detailed evaluation work room to remain legible.

Best For

  • Step-by-step resume reading
  • Detailed evaluation project records
  • Full-width project descriptions
  • Education and credentials in sequence
  • Clearly separated content sections
 

#4 One Column Format with Photo Right Resume Example

The single-column body includes a photo, giving this version a visual profile while keeping resume content in one reading sequence.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • The single-column body keeps project experience and education in a clear sequence.
  • A continuous reading path helps reviewers move between project results and credentials.
  • The photo offers a visual identity cue without changing the resume's section order.

Best For

  • Photo-inclusive resume preference
  • Single-path section review
  • Project results with context
  • Chronological academic experience
  • Text-heavy resume content
 

#5 Clean Two Column Sidebar Format Resume Example

The two-column sidebar presentation suits readers who want resume sections arranged in separate, nearby areas.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Two reading areas let reviewers scan experience separately from other resume sections.
  • A divided layout gives project achievements and education distinct places in the content flow.
  • The sidebar-style structure makes supporting skills and credentials easy to locate.

Best For

  • Sidebar-style content organization
  • Multiple resume sections
  • Skills and credential overview
  • Project details with supporting information
  • Separated content areas
 
 

#6 Two Column Format Resume Example

Two columns give this resume a side-by-side structure, useful when a reviewer wants related sections available at once.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • The two-column arrangement gives project history and education separate reading areas.
  • Reviewers can locate skills and credentials without moving through every experience detail.
  • Parallel sections keep supporting information visible alongside the broader resume content.

Best For

  • Side-by-side resume sections
  • Several project records
  • Quick access to credentials
  • Experience with supporting details
  • Readers comparing content areas
 

#7 Photo Header Two Column Format Resume Example

A header photo pairs with a two-column resume body, suiting readers who want a visual profile and separate content areas.

 
Resume example — page 1   Resume example — page 2

Why This Format Works

  • Two body columns separate project experience from other resume content for easier navigation.
  • Distinct reading areas help reviewers find education and credentials alongside experience details.
  • The header photo adds a visual identity cue while the columns organize resume sections.

Best For

  • Photo-led header preference
  • Divided resume content
  • Project history with technical details
  • Education beside other sections
  • Visual profile with section separation
 
 
 

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

ResumeInMinutes helps you organize project details, education, skills, and certifications into a clear resume. Compare image previews, then open an example in the editor to customize its content and layout.

  • ATS-friendly section structure
  • Clear project and skills sections
  • Relevant keyword guidance
  • Space for measurable results
  • Image previews of resume designs
  • Editor access to customize an example

Show how you connect LLMs with documents, APIs, and business systems. Use precise bullets to explain your RAG pipeline, agent workflows, prompt tests, and the results of your evaluations.

 

See Also

 

Frequently Asked Questions

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

Choose a design with readable headings and a clear section order. Use role-relevant terms such as AWS Bedrock, Strands, MCP, LLMs, RAG, REST APIs, and AI output evaluation when they accurately describe your skills and project work.

What does this resume example show?

It shows one resume example in different visual layouts. Compare the image previews, then open an example in the editor to view or customize it.

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

Include a focused summary, education, relevant projects or experience, technical skills, tools, and certifications. Use bullets to describe agent workflows, RAG steps, API integrations, testing, and documented results.

Which resume layout works well for this role?

A simple layout with clear headings makes education, project details, and technical skills easy to scan. Keep the design consistent and make sure important information is presented as readable text.

How can I show relevant experience if I have not held an AI engineering job?

Include coursework, research, and class projects that demonstrate useful skills. Describe what you built, the tools you used, how you tested it, and what you learned from the results.

Should I include a photo on my resume?

For U.S. applications, a resume without a photo is generally the safer choice. Follow local norms if you apply in another country.

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 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: 28 of 31 (90%) · Resume quality: 100%

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

Details
  • ✓Name present: Noah Sanchez
  • ✓Email format: noah.sanchez@example.com
  • ✓Phone format: (608) 555-XXXX
  • ✓Experience 1: Job title: AI Engineering Project Researcher
  • ✓Experience 1: Company name: University Project
  • ✓Experience 1: Work location: Madison, WI
  • ✓Experience 1: Employment dates: September 2025 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: RAG Evaluation Course Project
  • ✓Experience 2: Company name: Academic Research
  • ✓Experience 2: Work location: Madison, WI
  • ✓Experience 2: Employment dates: January 2025 - August 2025
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: AI-Powered Web Application Developer
  • ✓Experience 3: Company name: Course Project
  • ✓Experience 3: Work location: Madison, WI
  • ✓Experience 3: Employment dates: September 2024 - December 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 Resume 100% · Weight 20%

Parsed text
Noah Sanchez
(608) 555-XXXX
noah.sanchez@example.com
noahsanchez
Madison, WI 53703
SUMMARY
Computer Science undergraduate pursuing a B.S. with academic and project experience in AI engineering, generative AI, and software development. Build AI agents, agentic workflows, RAG pipelines, and AI-powered web applications that connect LLMs with documents, REST APIs, and simulated business systems. Test prompts and retrieval quality, document implementation choices, and bring project findings to classmates and faculty for review. Contribute to code reviews and design discussions while translating user needs into practical technical features. Current projects use AWS Bedrock, Strands, and MCP to explore AI applications and productivity tools. Pursuing a degree at the University of Wisconsin-Madison and authorized to work full time in the United States.
EDUCATION
Bachelor of Science in Computer Science, in progress
2027
University of Wisconsin-Madison
Madison, WI
TECHNICAL SKILLS
Programming Languages: Python, JavaScript, TypeScript
Web Technologies: HTML, CSS, React
Backend Frameworks: FastAPI, Flask, Express.js
Cloud Services: AWS Bedrock, AWS Lambda, Amazon S3
AI Agent Technologies: Strands, Model Context Protocol, LangChain
Machine Learning Libraries: Transformers, scikit-learn, PyTorch
Vector Databases: FAISS, Chroma, Pinecone
Data Processing: Pandas, NumPy, Apache Spark
Databases: SQLite, PostgreSQL, Amazon DynamoDB
Testing and API Tools: pytest, Postman, Swagger
Version Control: Git, GitHub, GitLab
SKILLS
AI agent development
Agentic workflows
Prompt engineering
RAG pipeline development
AI output evaluation
REST API integration
Requirements gathering
Technical documentation
Code review
Problem solving
Attention to detail
Cross-functional communication
Systematic testing
Information retrieval
AI web applications
EXPERIENCE
AI Engineering Project Researcher
September 2025 - Present
University Project
Madison, WI
Built a prototype agentic workflow for a campus knowledge assistant and AI productivity tool using AWS Bedrock, Strands, and the Model Context Protocol (MCP), testing tool calls against curated student-service scenarios.
Developed a RAG pipeline that ingests course and policy documents, creates embeddings, retrieves relevant passages, and supplies cited context to an LLM; evaluated answer quality against a manually reviewed question set.
Connected the prototype to REST APIs and simulated business systems, documenting API behavior, failure cases, and safeguards for data access.
Designed repeatable prompt and output tests to compare response relevance and grounding, then refined prompts and retrieval settings based on evaluation findings.
Presented design options and test results during project reviews, incorporating feedback from classmates and faculty into documented implementation changes.
RAG Evaluation Course Project
January 2025 - August 2025
Academic Research
Madison, WI
Evaluated retrieval-augmented generation approaches for a collection of technical support documents, comparing chunking, embedding, and retrieval choices against a labeled query set.
Created a small evaluation harness to record retrieval relevance and answer support, using error reviews to identify gaps in source coverage and improve quality evaluation.
Produced clean, documented code and a concise methods report that made the experiments reproducible for a faculty review.
AI-Powered Web Application Developer
September 2024 - December 2024
Course Project
Madison, WI
Created a web application prototype that used REST APIs to combine course scheduling data with a third-party calendar service, translating user needs into tested application features.
Wrote and documented application code, reviewed changes with project teammates, and discussed interface and system design tradeoffs during weekly reviews.
Tested API error handling and edge cases, then presented the finished prototype and implementation notes to the course group.
LEADERSHIP, AWARDS & RECOGNITION
Computer Sciences Department Undergraduate Project Showcase, 2025: Received faculty recognition for clearly presenting a reproducible RAG evaluation project.
Dean's List, University of Wisconsin-Madison, Fall 2024 and Spring 2025.
Facilitated weekly study group sessions for Computer Sciences courses in 2025-2026, sharing project documentation practices with classmates.
Volunteered at campus computing help desk orientation events in 2024-2025, guiding new students through basic account and software setup.
CERTIFICATIONS
AWS Academy Cloud Foundations
2025
Generative AI with Large Language Models, DeepLearning.AI
2025
PROFESSIONAL AFFILIATIONS
Student member, Association for Computing Machinery (ACM), participating in a professional community focused on computing and software development.
Student member, Institute of Electrical and Electronics Engineers (IEEE), engaging with a professional community spanning computing and engineering.

Matched keywords (28)

  • AI agents
  • agentic workflows
  • retrieval-augmented generationOriginal keyword in JD: Retrieval-augmented generation (RAG)
  • AWS Bedrock
  • Strands
  • MCPOriginal keyword in JD: Model Context Protocol (MCP)
  • LLMsOriginal keyword in JD: LLMs OR generative AI concepts OR prompt engineering
  • AI-powered web applications
  • AI productivity toolOriginal keyword in JD: AI productivity tools
  • REST APIs
  • used REST APIs to combine course scheduling data with a third-party calendar serviceOriginal keyword in JD: Third-party service integration
  • Connected the prototype to REST APIs and simulated business systemsOriginal keyword in JD: Business systems integration
  • ingests course and policy documentsOriginal keyword in JD: Data ingestion
  • embeddings
  • Information retrieval
  • evaluated answer quality against a manually reviewed question setOriginal keyword in JD: AI output quality evaluation
  • Designed repeatable prompt and output tests to compare response relevance and groundingOriginal keyword in JD: Systematic solution testing
  • refined prompts and retrieval settings based on evaluation findingsOriginal keyword in JD: Prompt refinement
  • Produced clean, documented codeOriginal keyword in JD: Readable, well-documented code
  • code reviews
  • discussed interface and system design tradeoffsOriginal keyword in JD: Technical design discussions
  • Requirements gathering
  • translating user needs into practical technical featuresOriginal keyword in JD: Translating business needs into technical solutions
  • Bachelor of Science in Computer Science, in progressOriginal keyword in JD: Currently pursuing a bachelor's or advanced degree in Computer Science OR Software Engineering OR Data Science OR Artificial Intelligence/Machine Learning OR a related discipline
  • authorized to work full time in the United StatesOriginal keyword in JD: Full-time U.S. work authorization
  • Problem solving
  • Attention to detail
  • Presented design options and test results during project reviews, incorporating feedback from classmates and faculty into documented implementation changes.Original keyword in JD: Written and verbal communication

Missing keywords (3)

  • LLM integration with enterprise data
  • Cross-functional collaboration
  • Self-motivation
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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