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7 AI/ML Engineer - Media and Audience Intelligence CV Templates with Example

By Ria · Last Updated On 23 October 2026
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This guide shows you how to create an AI/ML Engineer - Media and Audience Intelligence CV that is clear, focused and easy to review. You will learn how to organise relevant evidence so readers can see the value of your work.

For this role, connect machine learning delivery to audience insight, content discovery and engagement. Show how you turn high-volume media data into dependable models, and make your contribution clear through focused bullets and measurable results.

Compare the example 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, useful CV sections and role-relevant terms such as Python, PyTorch, MLOps, CI/CD, audience segmentation and content recommendations.

What Makes a Great AI/ML Engineer - Media and Audience Intelligence CV in 2026

A strong CV shows how you build, deploy and monitor machine learning products for media and audience needs. Clear experience bullets link your technical work to useful product outcomes.

Include relevant tools and terms, such as Python, TensorFlow, PyTorch, Scikit-learn, cloud platforms, MLOps and CI/CD. Add measurable 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 keeps the CV in one reading sequence, giving reviewers a direct route through experience, skills and education.

 
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Why This Format Works

  • Employment history follows one sequence, making changes in responsibility easier to compare.
  • Achievement bullets sit in the same flow as the roles they support.
  • Skills, education and credentials remain easy to locate within that sequence.

Best For

  • ATS-oriented reading order
  • Long, chronological work histories
  • Detailed achievement bullets
  • Skills, education and credentials together
  • Text-first application materials

CV Keywords

  • AI/ML Engineer with 11 years of experience delivering production machine learning for streaming, publishing and digital media. Build audience segmentation and content recommendation products, turning behavioural and commercial questions into practical solutions. Develop scalable pipelines for high-volume media data and manage model deployment and monitoring across cloud environments.
  • PyTorch
  • AWS
  • MLOps
  • CI/CD
  • Established MLOps workflows for model testing, deployment automation and monitoring
  • Model monitoring
  • deployment automation
  • Built scalable AWS data and machine learning pipelines
  • audience
  • audience segmentation
  • content recommendation
  • content discovery
  • Stakeholder engagement
  • Worked with commercial stakeholders and product teams to translate publishing objectives into technical specifications, model evaluation criteria and delivery plans.
See all keywords
 

#2 Simple Two Column Format CV Example

The Simple Two Column Format divides information between two columns, suiting CVs where reviewers need a clear distinction between career history and supporting details.

 
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Why This Format Works

  • Career history can be followed while supporting information remains in a separate column.
  • Section areas help reviewers locate skills, education and credentials by type.
  • Achievement details stay distinct from the wider record of experience.

Best For

  • Career history with supporting details
  • Quick comparison of CV sections
  • CVs with several skills and credentials
  • Parallel review of roles and evidence
  • Structured technical career records

#3 Plain One Column with Shaded Labels CV Example

The Plain One Column with Shaded Labels format keeps CV content in one column, a practical choice for direct top-to-bottom review.

 
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Why This Format Works

  • The single reading path helps reviewers follow career chronology from role to role.
  • Role details and achievement bullets remain together for context.
  • Education and credentials are easy to find within the same sequence.

Best For

  • Uninterrupted career chronology
  • Section-by-section CV review
  • Detailed role achievements
  • Combined education and credential records
  • Top-to-bottom reviewer preference
 

#4 One Column Format with Photo Right CV Example

The One Column Format with Photo Right includes a photo alongside a one-column CV body, for readers who prefer a portrait and a single reading path.

 
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Why This Format Works

  • The one-column body lets reviewers follow experience in a clear sequence.
  • Related role details and achievements remain together during review.
  • Skills and education fit into the same reading path as employment history.

Best For

  • Portrait-inclusive CV presentation
  • One-column career records
  • Sequential achievement review
  • Experience, skills and education in one flow
  • CVs where a photo is preferred
 

#5 Clean Two Column Sidebar Format CV Example

The Clean Two Column Sidebar Format uses two columns to separate career details from supporting information, helping reviewers move between the CV's main content areas.

 
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Why This Format Works

  • Career progression remains distinct from supporting sections in the two-column body.
  • Reviewers can locate education and credentials without losing the role sequence.
  • Skills and experience have separate areas for focused scanning.

Best For

  • Distinct CV information areas
  • Experience with concise supporting details
  • Skills and credentials beside career history
  • Section-based CV scanning
  • Text-led two-column documents
 
 

#6 Two Column Format CV Example

The Two Column Format presents the CV in two columns, suiting readers who want to compare career information with supporting details at a glance.

 
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Why This Format Works

  • Separate columns give career history and supporting content distinct reading areas.
  • Reviewers can follow role progression without overlooking education and credentials.
  • The arrangement helps distinguish achievement evidence from skills information.

Best For

  • Side-by-side information review
  • Career history with supporting records
  • Separate skills and education content
  • Two-column CV reading preference
  • Multi-section technical CVs
 

#7 Photo Header Two Column Format CV Example

The Photo Header Two Column Format combines an included photo with a two-column CV body, for readers who want a portrait alongside separate content areas.

 
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Why This Format Works

  • The two-column body keeps career history distinct from supporting information.
  • Reviewers can compare experience and credentials across separate content areas.
  • The photo provides a clear visual reference without changing the CV's reading order.

Best For

  • Photo-inclusive application documents
  • CVs with several content sections
  • Career history beside supporting details
  • Visual comparison of experience and credentials
  • Two-column reading preference
 
 
 

Why Build Your AI/ML Engineer - Media and Audience Intelligence CV with ResumeInMinutes

ResumeInMinutes helps you arrange machine learning experience, technical 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 role-specific keywords
  • Organised skills and experience
  • PNG image previews
  • CV customisation in the editor

Choose a format that makes your evidence easy to follow. Keep the focus on production models, scalable data pipelines, model monitoring and work with product and data teams.

 

See Also

 

Frequently Asked Questions

Are these AI/ML Engineer - Media and Audience Intelligence CV templates ATS-friendly?

The examples use clear headings and sections to support quick scanning. Include relevant terms naturally, such as production machine learning, audience segmentation, content recommendations, Python, cloud platforms and MLOps.

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 preferred example in the editor to customise it.

What should an AI/ML Engineer - Media and Audience Intelligence CV include?

Include a focused summary, relevant experience, technical skills and education. Show evidence of production model development, scalable pipelines, model testing and monitoring, audience insight and collaboration with product and data teams.

Which CV layout works well for this role?

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

How can I show machine learning achievements on my CV?

Use concise bullet points to explain the problem, your contribution and the result. Where you have reliable evidence, include measures linked to model quality, audience response, delivery or product outcomes.

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 results instead.

ATS score and CV checks

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ATS Score 92%

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: 19 of 22 (86%) · CV quality: 100%

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

Details
  • ✓Name present: Toby Hussain
  • ✓Email format: toby.hussain@example.com
  • ✓Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: Senior Machine Learning Engineer
  • ✓Experience 1: Company name: BrightWave Streaming
  • ✓Experience 1: Work location: Manchester, UK
  • ✓Experience 1: Employment dates: Aug 2022 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Machine Learning Engineer
  • ✓Experience 2: Company name: SignalFrame Publishing
  • ✓Experience 2: Work location: Bristol, UK
  • ✓Experience 2: Employment dates: Jan 2020 - Jul 2022
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Data Scientist
  • ✓Experience 3: Company name: Lantern Broadcast Group
  • ✓Experience 3: Work location: Cardiff, UK
  • ✓Experience 3: Employment dates: Jun 2017 - Dec 2019
  • ✓Experience 3: Description or achievements: Present
  • ✓Experience 4: Job title: Junior Data Analyst
  • ✓Experience 4: Company name: PixelReach Digital Media
  • ✓Experience 4: Work location: Nottingham, UK
  • ✓Experience 4: Employment dates: Sep 2015 - May 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
Toby Hussain
07700 90XXXX
toby.hussain@example.com
tobyhussain
Leeds, West Yorkshire LS7 2AB
SUMMARY
AI/ML Engineer with 11 years of experience delivering production machine learning for streaming, publishing and digital media. Build audience segmentation and content recommendation products, turning behavioural and commercial questions into practical solutions. Develop scalable pipelines for high-volume media data and manage model deployment and monitoring across cloud environments. Work with data scientists, data engineers, product teams and business stakeholders to shape requirements and evaluate results. Recent projects include assessing generative AI and large language models for content metadata enrichment, with quality checks and human review informing recommendations for production use.
EXPERIENCE
Senior Machine Learning Engineer
Aug 2022 - Present
BrightWave Streaming
Manchester, UK
Led development of audience segmentation and content recommendation models for a streaming catalogue, using Python, PyTorch and large-scale viewing and behavioural datasets to improve the relevance of discovery journeys.
Built scalable AWS data and machine learning pipelines combining customer, content and advertising signals, making refreshed features available to modelling and product teams on a reliable schedule.
Established MLOps workflows for model testing, deployment automation and monitoring, integrating CI/CD checks so production releases could be assessed consistently and issues identified promptly.
Partnered with data scientists, data engineers and product managers to turn audience engagement questions into model requirements, evaluation plans and measurable product experiments.
From 2024, evaluated generative AI and large language model prototypes for content metadata enrichment, applying quality checks and human review before recommending any production use.
Machine Learning Engineer
Jan 2020 - Jul 2022
SignalFrame Publishing
Bristol, UK
Developed Scikit-learn models for reader segmentation and article recommendations, joining subscription, content and behavioural datasets to support more relevant digital publishing experiences.
Implemented GCP-based data preparation and feature pipelines for high-volume content and customer records, documenting data quality checks for repeatable model development.
Introduced model validation and monitoring practices that gave editorial product teams clearer evidence of recommendation quality and changes in audience response.
Worked with commercial stakeholders and product teams to translate publishing objectives into technical specifications, model evaluation criteria and delivery plans.
Data Scientist
Jun 2017 - Dec 2019
Lantern Broadcast Group
Cardiff, UK
Analysed viewing and advertising datasets to identify audience patterns for broadcast and digital content teams, using Python and TensorFlow to develop and assess predictive models.
Produced reusable data preparation workflows and model evaluation reports, helping stakeholders compare audience segments and content performance using consistent measures.
Collaborated with data engineers and editorial product colleagues to refine data definitions and convert business questions into practical analytical solutions.
Junior Data Analyst
Sep 2015 - May 2017
PixelReach Digital Media
Nottingham, UK
Prepared customer, advertising and behavioural datasets for digital content analysis, using Python and SQL checks to resolve inconsistencies before reporting and modelling.
Built early Scikit-learn classification models under senior review, documenting assumptions and evaluation results for campaign and audience insight teams.
Presented clear findings to account and product colleagues, translating analysis into testable recommendations for digital engagement activity.
LEADERSHIP, AWARDS & RECOGNITION
Recognised by the BrightWave Streaming product group in 2025 for clear model evaluation guidance during a content discovery release.
Mentored two early-career analysts in Python, model validation and communicating findings to non-technical stakeholders.
EDUCATION
BSc Computer Science
2013
University of Sheffield
Sheffield, UK
CERTIFICATIONS
AWS Certified Machine Learning – Specialty
2024
Google Cloud Professional Machine Learning Engineer
2025
Microsoft Learn: Azure Machine Learning fundamentals
2023
TECHNICAL SKILLS
Programming Languages: Python, SQL, R
Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn
Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform
Data Processing: Apache Spark, Apache Beam, pandas
Data Orchestration: Apache Airflow, AWS Step Functions, Google Cloud Composer
Data Storage: Amazon S3, Google Cloud Storage, Azure Blob Storage
Databases: PostgreSQL, MySQL, BigQuery
Model Tracking: MLflow, Weights & Biases, TensorBoard
Containers: Docker, Kubernetes, Amazon ECS
CI/CD Platforms: Jenkins, GitHub Actions, GitLab CI/CD
Monitoring Tools: Prometheus, Grafana, Amazon CloudWatch
SKILLS
Machine learning
Python
TensorFlow
PyTorch
Scikit-learn
AWS
Azure
GCP
MLOps
CI/CD
Model monitoring
Audience segmentation
Content recommendations
Generative AI
Stakeholder engagement
PROFESSIONAL AFFILIATIONS
Member, British Computer Society, engaging with the UK computing profession and its machine learning community.
Member, Association for Computing Machinery, participating in its professional community for computing and artificial intelligence.
LANGUAGES
English (Native)
French (Beginner)

Matched keywords (19)

  • AI/ML Engineer with 11 years of experience delivering production machine learning for streaming, publishing and digital media. Build audience segmentation and content recommendation products, turning behavioural and commercial questions into practical solutions. Develop scalable pipelines for high-volume media data and manage model deployment and monitoring across cloud environments.Original keyword in JD: Production machine learning model development and deployment
  • PyTorchOriginal keyword in JD: TensorFlow OR PyTorch OR Scikit-learn
  • AWSOriginal keyword in JD: AWS OR Azure OR GCP
  • MLOps
  • CI/CD
  • Established MLOps workflows for model testing, deployment automation and monitoringOriginal keyword in JD: Machine learning lifecycle management
  • Model monitoring
  • deployment automation
  • Built scalable AWS data and machine learning pipelinesOriginal keyword in JD: Scalable data and machine learning pipelines
  • audienceOriginal keyword in JD: Large-scale customer OR audience OR content OR advertising OR behavioural datasets
  • audience segmentation
  • content recommendationOriginal keyword in JD: Content recommendations
  • content discovery
  • Stakeholder engagement
  • Worked with commercial stakeholders and product teams to translate publishing objectives into technical specifications, model evaluation criteria and delivery plans.Original keyword in JD: Business requirements translation
  • Partnered with data scientists, data engineers and product managersOriginal keyword in JD: Cross-functional collaboration
  • generative AI
  • large language models
  • streamingOriginal keyword in JD: Media OR publishing OR broadcasting OR streaming OR digital content experience

Missing keywords (3)

  • Advanced Python
  • Automated testing
  • Engagement optimisation
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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