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7 AI Engineering Manager - Generative AI Evaluation Resume Templates with Example

By Ria · Last Updated On September 22, 2026
Resume Example Page 1

This guide shows you how to build an AI Engineering Manager - Generative AI Evaluation resume that is focused, clear, and easy to review. You will learn how to organize leadership, quality engineering, and AI evaluation evidence across 7 ATS-friendly resume layouts.

Strong resumes for this role connect technical leadership with measurable quality practices. Give clear space to GenAI evaluation, software quality, test automation, observability, incident learning, cloud delivery, and the systems used to test non-deterministic model behavior.

The examples also show how to place experience, skills, projects, education, certifications, and tools so recruiters can scan them quickly. Use the format notes to choose a structure that fits the amount and type of evidence you need to present.

What Makes a Great AI Engineering Manager - Generative AI Evaluation Resume in 2026

A strong resume is clear, evidence-based, and easy to scan. It should show leadership scope, quality ownership, technical depth, and release impact without burying key facts.

Use role-specific terms such as GenAI evaluation, RAG quality, AI safety, human-in-the-loop validation, CI/CD, tracing, telemetry, and incident management when they match your real experience.

The examples shown below are intentionally detailed to demonstrate a range of experiences, skills, accomplishments and relevant keywords. We prefer detailed resumes as they provide more context to the hiring managers and also help the ATS systems identify relevant qualifications and score your resume better for a match. But the length of a resume is an individual choice. If you want a one page resume, just keep the most relevant experience and information for the job. 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 straight one-column reading order helps recruiters scan leadership, experience, skills, projects, education, and credentials without jumping between sections.

 
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Why This Format Works

  • Linear flow keeps career progression easy to follow from top to bottom.
  • Full-width sections give detailed experience and achievements enough room to breathe.
  • Skills and credentials remain distinct without competing with parallel body content.

Best For

  • Long leadership histories
  • Detailed experience bullets
  • Straight recruiter reading order
  • ATS-focused applications
  • Broad technical skill coverage

Resume Keywords

  • AI Engineering Leadership
  • generative AI evaluation
  • software quality
  • test automation
  • validation
  • observability
  • LLMs
  • SLMs
  • multimodal models
  • real time models
  • prompt engineering
  • agentic systems
  • RAG
  • vector databases
  • embeddings
See all keywords
 

#2 Plain One Column with Shaded Labels Resume Example

Shaded section labels create clear stops in a one-column flow, helping recruiters move quickly between experience, skills, projects, education, and credentials.

 
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Why This Format Works

  • Shaded labels separate major sections without breaking the single reading path.
  • Section boundaries help recruiters locate skills, education, and credentials faster.
  • Full-width experience keeps leadership evidence readable beside clearly marked transitions.

Best For

  • Section-heavy resumes
  • Fast section lookup
  • Readers who prefer clear boundaries
  • Mixed leadership and technical evidence
  • Applications needing restrained visual structure
 

#3 One Column Format with Photo Right Resume Example

A right-side photo keeps the body in one column while giving recruiters a clear, continuous path through experience, skills, education, and credentials.

 
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Why This Format Works

  • One-column body preserves a simple sequence for career history and achievements.
  • Photo stays in the header area without dividing the main experience flow.
  • Continuous section order helps recruiters connect experience, skills, and credentials logically.

Best For

  • Markets where resume photos are customary
  • Continuous career narratives
  • Applications needing personal visual identification
  • Experience sections with substantial detail
  • Readers who value uninterrupted body flow
 

#4 Clean Two Column Sidebar Format Resume Example

A clean sidebar creates two clear reading zones, letting recruiters separate compact skills and credentials from the deeper experience and achievement story.

 
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Why This Format Works

  • Sidebar separation keeps compact qualifications apart from detailed career progression.
  • Main column gives leadership experience and achievements sustained reading space.
  • Parallel zones make skills and credentials easier to find during quick review.

Best For

  • Dense skill inventories
  • Separate credential scanning
  • Long management experience
  • Recruiters comparing qualifications quickly
  • Resumes balancing depth and compact reference
 
 

#5 Two Column Format Resume Example

Balanced two-column organization helps recruiters compare detailed experience with supporting skills, education, projects, and credentials without losing the main career story.

 
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Why This Format Works

  • Two columns balance detailed experience with supporting qualifications across the page.
  • Career history can stay prominent while secondary sections remain easy to access.
  • Separated content groups help reviewers compare achievements, skills, education, and credentials.

Best For

  • Balanced information density
  • Strong experience plus supporting sections
  • Quick comparison of qualifications
  • Technical leadership resumes
  • Applications with varied supporting evidence
 

#6 Photo Header Two Column Format Resume Example

A photo header above two body columns gives recruiters visual identification first, then separates the detailed career story from supporting qualifications.

 
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Why This Format Works

  • Photo header identifies the person before the two-column body begins.
  • Body columns separate detailed experience from skills, education, and credentials.
  • Section division supports fast comparison while preserving a clear career narrative.

Best For

  • Regions where professional photos are expected
  • Resumes with many supporting qualifications
  • Readers comparing experience and credentials
  • Career histories needing a separate skills area
  • Applications using a formal photo header
 

#7 Simple Two Column Format Resume Example

A simple two-column structure reduces visual noise while helping recruiters scan experience alongside skills, education, projects, and credentials in a compact layout.

 
Resume Example Page 1   Resume Example Page 2

Why This Format Works

  • Simple column structure keeps dense information organized without heavy visual treatment.
  • Experience remains readable while supporting sections gain their own compact space.
  • Clear grouping helps recruiters move between achievements, skills, projects, and credentials.

Best For

  • Compact executive resumes
  • Subtle visual organization
  • Mixed technical and leadership content
  • Applications needing efficient space use
  • Readers who prefer simple section grouping
 
 

Why Build Your AI Engineering Manager - Generative AI Evaluation Resume with ResumeInMinutes

ResumeInMinutes helps you turn complex AI engineering leadership into a structured resume that is easier to scan, edit, and target. It gives you a practical way to separate leadership evidence, technical skills, delivery practices, projects, education, and certifications.

  • ATS-friendly section structure
  • Leadership and technical evidence organization
  • GenAI evaluation keyword guidance
  • Experience and project section support
  • Free PDF resume previews
  • Resume customization support

You can tailor the same core resume toward engineering leadership roles that value software quality, GenAI evaluation, hybrid cloud delivery, observability, and disciplined release practices, while keeping each claim grounded in your actual background.

 

See Also

 

Frequently Asked Questions

Are these AI Engineering Manager - Generative AI Evaluation resume templates ATS-friendly?

They use clear headings and structured resume sections that are easy for applicant tracking systems and recruiters to read. Keep your own wording aligned with the job description and your real experience.

Are these different resumes?

No. The guide shows one AI Engineering Manager - Generative AI Evaluation resume example in different visual formats, so you can compare how the same content reads in each layout.

What should an AI Engineering Manager - Generative AI Evaluation resume include?

Include a focused summary, leadership experience, quality engineering results, GenAI evaluation work, skills, tools, projects, education, certifications, and evidence of release or incident practices when supported by your background.

Can I download the resume templates as PDFs?

Yes. Each resume style provides a free PDF download so you can review the format before choosing a layout for your application.

Which resume format works well for this role?

A clear one-column format can support a direct reading order, while a two-column format can separate dense skills or credentials from detailed experience. Choose the structure that keeps leadership and technical evidence easy to follow.

How should I show GenAI evaluation experience on my resume?

Describe the evaluation approach, the quality or safety problem, the testing method, and the release or operational result. Use specific terms such as RAG quality, human review, model grading, tracing, or telemetry only when they match your work.

Should I include a photo on my resume?

Include a photo only when it is customary or expected in the country or market where you are applying. For many U.S. applications, a no-photo resume is the more common choice.

Can I customize these resume templates?

Yes. You can adjust the resume text, layout, and design while keeping the content focused on the exact AI Engineering Manager - Generative AI Evaluation role.

ATS score 82%

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 = 100 × (confirmed matches + 0.8 × possible matches) ÷ total keywords, rounded and capped at 100. Possible matches still need checking. Saved generation audits use their saved keyword percentage.
  • 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%

Live keyword match: 50 of 71 (70%) · Resume quality: 100%

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

Details
  • ✓Name present: Ezekiel Olson
  • ✓Email format: ezekiel.olson@example.com
  • ✓Phone format: (630) 555-3172
  • ✓Experience 1: Job title: Director, AI Quality Engineering
  • ✓Experience 1: Company name: Praxis AI Platforms
  • ✓Experience 1: Employment dates: February 2023 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Senior Engineering Manager, Software Quality and AI Validation
  • ✓Experience 2: Company name: Atlas Industrial Software
  • ✓Experience 2: Employment dates: May 2020 - January 2023
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Principal Software Development Engineer in Test
  • ✓Experience 3: Company name: Northline Cloud Systems
  • ✓Experience 3: Employment dates: July 2017 - April 2020
  • ✓Experience 3: Description or achievements: Present
  • ✓Experience 4: Job title: Software Quality Engineer
  • ✓Experience 4: Company name: Lakefront Systems Engineering
  • ✓Experience 4: Employment dates: September 2014 - June 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 Resume 100% · Weight 20%

Parsed text
Ezekiel Olson
(630) 555-3172
ezekiel.olson@example.com
linkedin.com/example/ezekielolson
Naperville, IL 60540
SUMMARY
AI engineering leader with 12 years of software quality and platform experience, including more than five years guiding teams across test automation, validation, observability, and generative AI evaluation. Leads enterprise software and GenAI delivery across hybrid cloud and edge connected environments through disciplined SDLC practices, CI/CD, progressive deployment, incident management, and measurable quality controls. Builds evaluation strategies for non deterministic systems using deterministic checks, probabilistic assessment, RAG quality measures, safety testing, human review, A/B testing, tracing, telemetry, and root cause analysis. Hands on with LLMs, SLMs, multimodal and real time models, agentic systems, prompt engineering, vector databases, embeddings, LoRA, MCP, A2A, and synthetic data. Creates clear priorities, develops engineers, and connects technical evidence with product risk, release confidence, and enterprise outcomes.
EXPERIENCE
Director, AI Quality Engineering
Praxis AI Platforms
February 2023 - Present
Chicago, IL
Lead an AI quality engineering group responsible for enterprise agents, digital assistants, RAG services, and multimodal capabilities across cloud and edge connected products. Set technical direction, define evaluation evidence, develop engineering capability, and align release decisions with product risk across Azure, AWS, and GCP.
Led layered evaluation framework for agents and RAG services, improving release evidence for non deterministic outputs.
Defined safety checks, retrieval measures, human review, and model grading for enterprise GenAI capabilities.
Scaled CI/CD quality gates, feature flags, canary releases, and regression suites across multiple AI products.
Connected Langfuse, Phoenix, tracing, and telemetry with prompts, retrieval context, tools, and downstream failures.
Guided engineers through MCP, A2A, vector search, embeddings, chunking, LoRA, and real time model testing.
Partnered with product leaders to set quality metrics, risk thresholds, and release evidence across cloud environments.
Senior Engineering Manager, Software Quality and AI Validation
Atlas Industrial Software
May 2020 - January 2023
Oak Brook, IL
Managed software quality, automation, and validation engineers supporting cloud services and embedded edge applications for connected industrial products. Built team capability, standardized SDLC and release practices, and connected validation evidence with production risk across several product lines.
Managed quality engineers supporting cloud services and embedded edge applications for connected industrial products.
Built API, event flow, and device interaction tests, integrating coverage into CI/CD pipelines and release controls.
Introduced blue/green and canary validation playbooks with feature flags before broad production releases.
Led incident root cause reviews using logs, distributed traces, test history, and deployment changes.
Converted recurring failures into automated scenarios and expanded validation coverage for complex workflows.
Developed engineers through mentoring, architecture reviews, quality metrics, and cross product release standards.
Principal Software Development Engineer in Test
Northline Cloud Systems
July 2017 - April 2020
Chicago, IL
Designed test platforms for enterprise cloud applications spanning APIs, data pipelines, user workflows, and distributed integrations. Mentored test engineers, improved pre release evidence, and supported early machine learning evaluation through repeatable criteria and human review.
Designed test platforms for cloud APIs, data pipelines, user workflows, and distributed enterprise integrations.
Integrated functional, reliability, and performance checks into build and deployment pipelines before release decisions.
Developed reusable test services and telemetry utilities that supported fault isolation across distributed systems.
Partnered with software engineers on testable interfaces, observability hooks, and deployment safeguards during development.
Evaluated NLP and machine learning services with curated datasets, acceptance criteria, and human output review.
Mentored test engineers on automation architecture, root cause analysis, software design, and risk based validation.
Software Quality Engineer
Lakefront Systems Engineering
September 2014 - June 2017
Evanston, IL
Built automated quality practices for enterprise software delivered through structured SDLC cycles and frequent releases. Supported integration testing, release validation, defect triage, test data, and operational readiness across distributed application environments.
Built automated integration and regression tests for enterprise products delivered through structured SDLC cycles.
Created service interface and data workflow harnesses with deterministic assertions and diagnostic logging.
Supported release validation and defect triage by linking behavior with code changes and test evidence.
Expanded continuous integration coverage for critical workflows used by development teams.
Maintained reliable test data and execution environments across evolving distributed application platforms.
Documented reproducible quality procedures for product requirements, testability, and operational readiness reviews.
PROJECTS
Enterprise GenAI Evaluation Framework
2026
Built a layered evaluation approach for agents, RAG services, and multimodal systems using deterministic checks, probabilistic grading, safety review, retrieval measures, telemetry, and human validation.
Hybrid Cloud Release Quality Program
2023
Established shared validation practices for cloud and edge connected products, combining CI/CD gates, feature flags, progressive deployment, incident learning, and production quality evidence.
LEADERSHIP & AWARDS
AI Quality Leadership Recognition, Praxis AI Platforms, 2025
Engineering Excellence Award, Atlas Industrial Software, 2022
Technical Mentorship Recognition, Northline Cloud Systems, 2019
EDUCATION
Master of Science in Computer Science
Illinois Institute of Technology
GPA: 4.0
2014
Chicago, IL
Coursework: Artificial Intelligence, Software Engineering, Distributed Systems, Machine Learning
Bachelor of Science in Software Engineering
Iowa State University
GPA: 4.0
2012
Ames, IA
Coursework: Software Testing, Database Systems, Algorithms, Systems Design
CERTIFICATIONS
AWS Certified Machine Learning Engineer - Associate
2026
Microsoft Certified: Azure AI Engineer Associate
2025
TECHNICAL SKILLS
Cloud Platforms: Azure, AWS, GCP
AI Cloud Services: Azure AI Foundry, SageMaker, Bedrock
Generative AI Models: LLMs, SLMs, multimodal models
AI Architectures: RAG, vector databases, embeddings
Agent Platforms: LangChain, LangGraph, MCP
Evaluation Platforms: Langfuse, LangSmith, Humanloop
Evaluation Frameworks: Ragas, DeepEval, Phoenix
Deployment Practices: CI/CD, blue/green deployment, canary deployment
Quality Engineering: test automation, software QA, validation
Observability Systems: tracing, telemetry, incident management
Model Adaptation: prompt engineering, LoRA, chunking
Testing Methods: A/B testing, synthetic data, human review
SKILLS
AI Engineering Leadership
GenAI Evaluation
Test Automation
Software Quality Assurance
LLMs
SLMs
Multimodal Models
Agentic Systems
Prompt Engineering
RAG
Vector Databases
Embeddings
LoRA Fine Tuning
Human Validation
AI Safety Evaluation
PROFESSIONAL AFFILIATIONS
Member, Association for Computing Machinery, AI and software engineering community
Member, IEEE Computer Society, software quality and systems engineering community
LANGUAGES
English (Native)
Spanish (Intermediate)
ADDITIONAL INFORMATION
Work Status
: Authorized to work in United States. No sponsorship required.
REFERENCES
AVAILABLE ON REQUEST

Matched keywords (50)

  • AI Engineering Leadership
  • generative AI evaluation
  • software quality
  • test automation
  • validation
  • observability
  • LLMs
  • SLMs
  • multimodal models
  • real time models
  • prompt engineering
  • agentic systems
  • RAG
  • vector databases
  • embeddings
  • LoRA Fine Tuning
  • MCP
  • A2A
  • Safety Evaluation
  • A/B testing
  • tracing
  • telemetry
  • root cause analysis
  • Azure
  • AWS
  • GCP
  • Azure AI Foundry
  • SageMaker
  • Bedrock
  • LangChain
  • LangGraph
  • Langfuse
  • LangSmith
  • Humanloop
  • Ragas
  • DeepEval
  • Phoenix
  • hybrid cloud
  • CI/CD
  • incident management
  • feature flags
  • blue/green deployment
  • canary deployment
  • SDLC
  • Software Quality Assurance
  • Software Development
  • enterprise software
  • Engineering Leadership
  • technical direction
  • quality metrics

Missing keywords (21)

  • speech models
  • tool use
  • orchestration
  • chunking strategies
  • metric design
  • RAG quality assessment
  • output reliability
  • human in the loop validation
  • deterministic testing
  • probabilistic testing
  • evaluation data collection
  • AI observability
  • automated test generation
  • synthetic data creation
  • scenario expansion
  • LLM assisted evaluation
  • Snowflake Cortex
  • Arize
  • embedded edge environments
  • automated testing
  • team performance
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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