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7 Senior AI Engineer - RAG and AI Assistants CV Templates with Example

By Ria · Last Updated On 23 October 2026
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This guide shows you how to create a clear, focused CV for a Senior AI Engineer - RAG and AI Assistants role. You will learn how to present relevant evidence so recruiters can quickly see your fit for the work.

Strong examples connect RAG and AI assistant delivery to practical results, such as testing document retrieval, checking completeness and moving proofs of concept towards secure services. We also cover how to present model evaluation, Azure AI tools, enterprise integration and responsible AI in a way that is easy to scan.

Explore the example presented in 7 layouts, compare the PNG image previews, and open a preferred example in the editor to customise it. The guide also explains ATS readability, useful CV sections and keywords for this specialist role.

What Makes a Great Senior AI Engineer - RAG and AI Assistants CV in 2026

A strong CV makes AI delivery, technical decisions and results easy to follow. Use concise experience bullets to show how you tested assistants and moved useful proofs of concept towards reliable services.

Include relevant terms such as Retrieval Augmented Generation, Azure AI Foundry, Azure Machine Learning, MLflow, model evaluation and responsible AI. Support claims with clear evidence, including measurable results where available.

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 presents CV content in one clear sequence, helping reviewers follow each section from top to bottom.

 
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Why This Format Works

  • A single-column sequence lets reviewers follow career progression without switching between parallel areas.
  • Consistent section order makes experience, education and credentials easy to locate.
  • Full-width text gives achievement bullets room to explain outcomes and context.

Best For

  • Single-column reading order
  • Chronological career history
  • Detailed achievement bullets
  • Supporting sections in sequence
  • ATS-oriented text structure

CV Keywords

  • AI application development
  • Retrieval Augmented Generation
  • Develop AI assistants and Retrieval Augmented Generation solutions
  • AI assistants
  • Leads delivery of AI assistants and workflow proofs of concept for higher education and public sector clients
  • Move proofs of concept towards secure, maintainable services
  • AI workflow automation
  • prompt engineering
  • model configuration
  • AI application testing
  • configured the selected service and API deployment path
  • Compared candidate models against accuracy, latency and usage cost criteria for a design review workflow, then configured the selected service
  • Retrieval tests and structured evaluations
  • Integrated LLM and AI services with enterprise APIs and document workflows
  • API integration
See all keywords
 

#2 Simple Two Column Format CV Example

Simple Two Column Format places CV information in two columns, giving reviewers a direct way to scan distinct content areas.

 
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Why This Format Works

  • Two columns let reviewers compare career history with supporting CV sections in one view.
  • Separate column areas help skills and education stand apart from experience.
  • Side-by-side reading suits CVs with several distinct information groups.

Best For

  • Side-by-side section review
  • Several distinct CV sections
  • Experience with supporting details
  • Compact information grouping
  • Scanning separate content areas

#3 Plain One Column with Shaded Labels CV Example

Plain One Column with Shaded Labels keeps the CV in a single column, so reviewers can follow sections in sequence.

 
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Why This Format Works

  • One-column reading keeps achievement bullets in a clear top-to-bottom order.
  • A consistent sequence helps reviewers locate experience, education and credentials.
  • The single-column structure keeps career history together for an uninterrupted review.

Best For

  • One-column section navigation
  • Continuous career history
  • Detailed achievement bullets
  • Sequential section review
  • Text-first CV presentation
 

#4 One Column Format with Photo Right CV Example

One Column Format with Photo Right includes a photo and keeps CV content in one column, suiting readers who prefer both features.

 
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Why This Format Works

  • One continuous column keeps experience and achievement bullets in a familiar reading order.
  • A single text sequence helps reviewers move from career history to education.
  • The photo adds a personal visual element without changing the CV's content order.

Best For

  • CVs with a photo
  • Single-column reading order
  • Career history with detailed bullets
  • Sequential supporting sections
  • A personal visual element
 

#5 Clean Two Column Sidebar Format CV Example

Clean Two Column Sidebar Format divides CV content across two columns, giving reviewers separate areas to scan.

 
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Why This Format Works

  • Two distinct areas let reviewers scan career history and supporting sections separately.
  • The column layout helps skills and education remain distinct from experience.
  • Separate content areas support quick movement between achievements and credentials.

Best For

  • Two-area information review
  • Experience with supporting details
  • Skills and education kept distinct
  • Quick access to CV sections
  • Several information groups
 
 

#6 Two Column Format CV Example

Two Column Format arranges CV information in two columns, offering a clear way to review several sections together.

 
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Why This Format Works

  • Two columns provide distinct areas for career history and supporting CV information.
  • Reviewers can move between achievements and skills while keeping section context.
  • Education and credentials remain easy to scan alongside the wider work history.

Best For

  • Two-column section grouping
  • Parallel review of CV sections
  • Career history with supporting details
  • Compact content organisation
  • Several information groups
 

#7 Photo Header Two Column Format CV Example

Photo Header Two Column Format combines a photo with a two-column CV body for readers who want both features.

 
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Why This Format Works

  • The two-column body separates career progression from supporting sections for a clear review path.
  • Two columns help reviewers compare experience with other CV information.
  • A photo adds a visual element while the body keeps its two-column reading structure.

Best For

  • CVs with a photo
  • Two-column content organisation
  • Supporting sections alongside experience
  • Visual element with structured content
  • Several CV information groups
 
 
 

Why Build Your Senior AI Engineer - RAG and AI Assistants CV with ResumeInMinutes

ResumeInMinutes lets you compare CV layouts using PNG image previews, then open an example in the editor to customise its content and appearance. Choose a structure that makes your technical experience easy to review.

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

Keep the focus on RAG systems, AI assistants, careful evaluation and dependable delivery. Make it easy to find your experience with cloud platforms, APIs, software engineering practices and responsible AI controls.

 

See Also

 

Frequently Asked Questions

Are these Senior AI Engineer - RAG and AI Assistants CV templates ATS-friendly?

The examples use readable headings and clear sections to support scanning. Include relevant terms naturally, such as Retrieval Augmented Generation, Azure AI Foundry, Azure Machine Learning, MLflow, model evaluation and CI/CD.

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 Senior AI Engineer - RAG and AI Assistants CV include?

Include a focused summary, relevant experience, technical skills, education and certifications. Show how you develop and evaluate RAG assistants, connect AI services to enterprise systems, and apply testing, monitoring and security controls.

Which CV layout works well for this role?

Choose a layout with clear headings and an easy-to-follow order for your experience, skills and education. A simple structure helps recruiters and applicant tracking systems find important details.

How can I show AI assistant achievements on my CV?

Use concise bullet points to explain the problem, your contribution and the result. Where you have evidence, include figures such as the 30% reduction in manual preparation time achieved during a pilot.

Should I include a photo on my CV?

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

ATS score and CV checks

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ATS Score 100%

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: 39 of 39 (100%) · CV quality: 100%

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

Details
  • ✓Name present: Arjun Richardson
  • ✓Email format: arjun.richardson@example.com
  • ✓Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: Senior AI Engineer
  • ✓Experience 1: Company name: Wrenfield Digital
  • ✓Experience 1: Work location: Bristol, UK
  • ✓Experience 1: Employment dates: Apr 2022 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Machine Learning Engineer
  • ✓Experience 2: Company name: Brackenridge Analytics
  • ✓Experience 2: Work location: Cardiff, UK
  • ✓Experience 2: Employment dates: Jan 2019 - Mar 2022
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Software Engineer
  • ✓Experience 3: Company name: Fieldstone Cloud Services
  • ✓Experience 3: Work location: Manchester, UK
  • ✓Experience 3: Employment dates: Jul 2017 - Dec 2018
  • ✓Experience 3: Description or achievements: Present
  • ✓Experience 4: Job title: Junior Software Developer
  • ✓Experience 4: Company name: Lattice Systems
  • ✓Experience 4: Work location: Exeter, UK
  • ✓Experience 4: Employment dates: Jun 2015 - Jun 2017
  • ✓Experience 4: 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
Arjun Richardson
07700 90XXXX
arjun.richardson@example.com
arjunrichardson
Bristol, BS1 5AH
SUMMARY
Senior AI Engineer with 11 years of experience delivering cloud software, machine learning services and practical AI solutions for complex organisations. Develop AI assistants and Retrieval Augmented Generation solutions that assess technical documents against infrastructure standards, identifying missing details before review. Move proofs of concept towards secure, maintainable services through testing, model evaluation and integration with enterprise systems. Collaborate with Enterprise Architecture, product owners and cyber security, data, infrastructure and networking specialists to clarify requirements, manage risks and shape responsible delivery. Apply Azure AI Foundry, Azure Machine Learning and MLflow alongside established software engineering practices, including automated testing, CI/CD and production monitoring. Bring experience serving higher education and public sector teams, supported by a computer science degree and current Azure AI credentials.
EXPERIENCE
Senior AI Engineer
Apr 2022 - Present
Wrenfield Digital
Bristol, UK
Leads delivery of AI assistants and workflow proofs of concept for higher education and public sector clients, translating product owner priorities into scoped releases and measurable acceptance criteria.
Since 2024, developed a RAG assistant using Azure AI Foundry and Azure Machine Learning to review cloud infrastructure designs against an approved document set. Retrieval tests and structured evaluations helped users identify missing configuration details and completeness issues before design review.
Refined prompt engineering and model configuration through test cases covering relevance, consistency and unsupported responses; documented limitations and responsible AI controls for operational handover.
Integrated LLM and AI services with enterprise APIs and document workflows, agreeing access and data handling with cyber security, data, integrations, infrastructure and networking colleagues.
Packaged services with Docker and Kubernetes, and established version control, automated testing, code review, CI/CD, monitoring and deployment documentation to support repeatable releases.
Applied governance standards, security controls, guardrails and model risk management reviews to delivery decisions, balancing quality, cost, maintainability and implementation speed while raising risks early.
Worked with Enterprise Architecture and AI Product Owners to assess cloud designs, shape follow-on use cases and create reusable implementation guidance as initial solutions moved towards production.
Built an AI application testing suite for the infrastructure design assistant, using versioned retrieval cases and expected response checks to identify document gaps and unsupported answers before deployment.
Compared candidate models against accuracy, latency and usage cost criteria for a design review workflow, then configured the selected service and API deployment path to meet agreed operational requirements.
Automated document intake and completeness checks by connecting the assistant to enterprise APIs, cutting manual preparation time by 30% across the pilot review queue.
Machine Learning Engineer
Jan 2019 - Mar 2022
Brackenridge Analytics
Cardiff, UK
Delivered Azure Machine Learning services for public sector operational teams, converting agreed analytical requirements into tested models and integration-ready components.
Introduced MLflow tracking for experiments and model versions, pairing documented evaluation criteria with reviewable release records to improve reproducibility and support model risk assessments.
Containerised scoring services with Docker and supported Kubernetes deployments, coordinating network access, infrastructure requirements and API integration with platform specialists.
Added automated tests, CI/CD checks and production monitoring, enabling teams to identify data-quality issues and model drift before they affected downstream workflows.
Presented model limitations, security considerations and implementation options to technical stakeholders, helping distinguish viable production use cases from exploratory proofs of concept.
Software Engineer
Jul 2017 - Dec 2018
Fieldstone Cloud Services
Manchester, UK
Built cloud-hosted services and API integrations for enterprise clients, converting technical requirements into maintainable components aligned with infrastructure and networking standards.
Used Docker to standardise development and test environments, and supported Kubernetes-based deployment work with platform engineers as client container platforms matured.
Strengthened version control, code review and automated testing practices across delivery work, improving the consistency of releases and the quality of handover documentation.
Worked with security and data teams to resolve access, configuration and information-handling issues before deployment.
Junior Software Developer
Jun 2015 - Jun 2017
Lattice Systems
Exeter, UK
Developed and tested backend features for internal business applications, using version control and peer review to deliver changes safely within scheduled releases.
Connected application components to REST APIs and relational data sources, documenting interface behaviour so support teams could diagnose integration issues.
Created repeatable build and test scripts for development environments, giving the team clearer feedback on regressions and reducing manual checks during release preparation.
LEADERSHIP, AWARDS & RECOGNITION
Recognised by a client delivery team in 2025 for clear technical documentation and a smooth assistant pilot handover.
Mentored two junior developers at Wrenfield Digital on testing practices, code review and maintainable API design.
EDUCATION
BEng Computer Science
2014
University of Exeter
GPA: 3.6
Exeter, UK
CERTIFICATIONS
Microsoft Certified: Azure AI Engineer Associate
2025
Microsoft Certified: Azure Data Scientist Associate
2022
Responsible AI: Applying AI Principles, Microsoft Learn
2024
TECHNICAL SKILLS
AI and Machine Learning: Azure AI Foundry, Azure Machine Learning, MLflow
Cloud Platforms: Microsoft Azure, Azure Blob Storage, Azure Functions
Container Platforms: Docker, Kubernetes, Helm
Programming Languages: Python, C#, SQL
API Technologies: REST, OpenAPI, JSON
Data Stores: Azure SQL Database, PostgreSQL, Microsoft SQL Server
CI/CD Tools: Azure DevOps, GitHub Actions, Jenkins
Version Control: Git, GitHub, Azure Repos
Testing Tools: pytest, unittest, Postman
Monitoring Tools: Azure Monitor, Application Insights, Prometheus
AI Frameworks: scikit-learn, TensorFlow, PyTorch
SKILLS
AI application development
RAG solutions
AI assistants
Prompt engineering
Model evaluation
AI workflow automation
Cloud design
Enterprise integration
Responsible AI
Model risk management
Security controls
Data governance
Risk identification
Production delivery
Technical documentation
PROFESSIONAL AFFILIATIONS
Member, British Computer Society (BCS), connecting with a professional community focused on computing practice and continuing development.
Member, Association for Computing Machinery (ACM), engaging with a professional community covering computer science and software engineering.

Matched keywords (39)

  • AI application development
  • Retrieval Augmented GenerationOriginal keyword in JD: Retrieval Augmented Generation (RAG)
  • Develop AI assistants and Retrieval Augmented Generation solutionsOriginal keyword in JD: Large Language Models (LLMs)
  • AI assistants
  • Leads delivery of AI assistants and workflow proofs of concept for higher education and public sector clientsOriginal keyword in JD: Proof-of-concept delivery
  • Move proofs of concept towards secure, maintainable servicesOriginal keyword in JD: Production-ready AI solutions
  • AI workflow automation
  • prompt engineering
  • model configuration
  • AI application testing
  • configured the selected service and API deployment pathOriginal keyword in JD: AI application deployment
  • Compared candidate models against accuracy, latency and usage cost criteria for a design review workflow, then configured the selected serviceOriginal keyword in JD: Model selection
  • Retrieval tests and structured evaluationsOriginal keyword in JD: AI evaluation
  • Integrated LLM and AI services with enterprise APIs and document workflowsOriginal keyword in JD: Enterprise system integration
  • API integration
  • Cloud Platforms
  • Azure AI Foundry
  • Azure Machine Learning
  • Introduced MLflow tracking for experiments and model versionsOriginal keyword in JD: MLOps
  • MLflow
  • Enterprise Architecture
  • Cloud design
  • cyber security
  • responsible AI
  • model risk management
  • security controls
  • Applied governance standards, security controls, guardrails and model risk management reviewsOriginal keyword in JD: AI guardrails
  • Data governance
  • infrastructure and networking
  • version control
  • code review
  • automated testing
  • CI/CD
  • production monitoring
  • technical documentation
  • Risk identification
  • create reusable implementation guidanceOriginal keyword in JD: Reusable delivery patterns
  • higher education
  • public sector

Missing keywords (0)

No missing keywords.

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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