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7 AI Research Engineer - Financial Crime Compliance 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 Research Engineer - Financial Crime Compliance CV that makes your evidence clear and easy to review. Learn how to organise your experience and present your contribution to this specific role.

Strong CVs for this role connect AI research and engineering work with practical results in financial crime compliance. Show how you evaluate models, develop LLM and agentic AI systems, apply prompt engineering, and use human feedback, benchmarking and MLOps. Include measurable outcomes where you can support them, such as reducing median case processing time from 11 minutes to 8 minutes or cutting manual evidence collation by 18% in a pilot.

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 role-specific keywords, including anti-money laundering (AML), foundation model fine-tuning, computer vision, back-testing and evaluation pipelines.

What Makes a Great AI Research Engineer - Financial Crime Compliance CV in 2026

A strong CV makes research, model delivery and measurable results easy to scan. Clear experience bullets show how your work supports reliable financial crime compliance processes.

Use relevant terms such as LLMs, agentic AI, model evaluation, back-testing and MLOps. Add figures only when your evidence supports 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 follows one vertical reading path, helping reviewers trace career history from role to role.

 
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Why This Format Works

  • A single reading path lets recruiters follow role changes across the full career history.
  • Experience bullets remain in sequence, so measurable results sit within their relevant roles.
  • The shared vertical flow links education and certifications to the career record.

Best For

  • Chronological career histories
  • Top-to-bottom reading
  • Detailed experience records
  • Achievement-led role entries
  • Sequential education and credentials

CV Keywords

  • machine learning
  • LLMs
  • agentic AI systems
  • foundation model fine-tuning
  • prompt engineering
  • financial crime compliance
  • Anti-money laundering
  • model evaluation
  • benchmarking
  • back-testing
  • evaluation pipelines
  • MLOps
  • Trained custom machine learning models
  • Human feedback loops
  • Established continuous improvement loops that incorporated human feedback from compliance analysts into model iteration and evaluation.
See all keywords
 

#2 Simple Two Column Format CV Example

The Simple Two Column Format gives career history and supporting details separate areas, so reviewers can see both at a glance.

 
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Why This Format Works

  • Role-by-role experience retains a clear chronology within the main career record.
  • Separate areas help reviewers locate education and credentials without searching each role entry.
  • The two-column layout lets readers compare career evidence with supporting skills.

Best For

  • Career history with supporting sections
  • Side-by-side section review
  • Skills and credentials near experience
  • Multi-section career records
  • Readers comparing distinct CV areas

#3 Plain One Column with Shaded Labels CV Example

The Plain One Column with Shaded Labels format keeps the CV in one vertical sequence, offering a direct route through experience and qualifications.

 
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Why This Format Works

  • A vertical reading path keeps experience entries in chronological order.
  • Education and certifications follow the career record in a simple sequence.
  • Continuous reading keeps achievement bullets connected to the roles they describe.

Best For

  • Continuous career timelines
  • Text-led CVs
  • Detailed role descriptions
  • Sequential education records
  • Straightforward recruiter scanning
 

#4 One Column Format with Photo Right CV Example

The One Column Format with Photo Right keeps career details in one reading path, with a photo set to the right as a visual reference.

 
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Why This Format Works

  • The right-side photo adds a visual reference without interrupting the single-column text.
  • Role-by-role experience keeps career progression visible in the main reading sequence.
  • Education and credentials follow the work history within the same text column.

Best For

  • CVs with a right-side photo
  • Single-column career records
  • Sequential experience details
  • Education after work history
  • Photo-led page design
 

#5 Clean Two Column Sidebar Format CV Example

The Clean Two Column Sidebar Format uses two body columns to keep supporting details visible alongside the career record.

 
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Why This Format Works

  • The split layout keeps supporting details visible while experience remains the main reference.
  • Related skills and credentials are easy to find without scanning every experience bullet.
  • Career history stays distinct from the supporting material that completes the CV.

Best For

  • Career records with supporting details
  • Separate skill and experience areas
  • Section-led CV reviews
  • CVs with varied evidence
  • Quick access to supporting sections
 
 

#6 Two Column Format CV Example

The Two Column Format sets out the CV in two reading areas, giving career history and supporting sections distinct space.

 
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Why This Format Works

  • Two columns place career history and supporting sections in separate reading areas.
  • Readers can find education or credentials without tracing the full work timeline.
  • Distinct column space keeps skills visible beside detailed role entries.

Best For

  • Career history beside qualifications
  • Multi-section CV content
  • Parallel section review
  • Detailed experience entries
  • Fast access to supporting information
 

#7 Photo Header Two Column Format CV Example

The Photo Header Two Column Format combines a photo in the header with two body columns, separating the career record from supporting details.

 
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Why This Format Works

  • Two-column body space separates career history from supporting details for direct comparison.
  • Role sequence remains clear while skills and credentials sit in another reading area.
  • The header photo adds a visual reference without changing the body’s two-column order.

Best For

  • Two-column career records
  • Supporting skills beside experience
  • Direct review of distinct sections
  • Photo header preference
  • CVs with varied content
 
 
 

Why Build Your AI Research Engineer - Financial Crime Compliance CV with ResumeInMinutes

ResumeInMinutes helps you organise AI research, engineering experience, skills and education in a clear CV. Compare PNG image previews, then open an example in the editor to customise its content and layout.

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

Choose a format that makes your model development and evaluation work easy to follow. Keep the focus on financial crime compliance, practical AI delivery and outcomes you can explain.

 

See Also

 

Frequently Asked Questions

Are these AI Research Engineer - Financial Crime Compliance CV templates ATS-friendly?

The examples use clear headings and sections that are easy to scan. Include relevant terms naturally, such as LLMs, agentic AI, foundation model fine-tuning, AML, model evaluation and MLOps.

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 an AI Research Engineer - Financial Crime Compliance CV include?

Include a focused profile, relevant experience, skills, education and certifications. Use bullet points to show model development, evaluation, production work and measurable results in financial crime compliance.

Which CV layout works well for this role?

Choose a layout with clear headings and a logical order for your experience, skills and education. A simple structure helps recruiters and applicant tracking systems find key details.

How can I show AI research achievements on my CV?

Write concise bullet points that state the task, your contribution and the result. Use supported measures, such as a change in processing time or manual work, and explain how you evaluated model quality.

Should I include a photo on my CV?

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

ATS score and CV checks

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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 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: 18 of 20 (90%) · CV quality: 100%

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

Details
  • ✓Name present: Toby Stewart
  • ✓Email format: toby.stewart@example.com
  • ✓Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: AI Research Engineer
  • ✓Experience 1: Company name: Cedarstone Analytics
  • ✓Experience 1: Work location: Bristol, UK
  • ✓Experience 1: Employment dates: Jan 2024 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Machine Learning Research Engineer
  • ✓Experience 2: Company name: North Quay Intelligence
  • ✓Experience 2: Work location: Bath, UK
  • ✓Experience 2: Employment dates: Sep 2022 - Dec 2023
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Machine Learning Engineer
  • ✓Experience 3: Company name: Lattice Risk Labs
  • ✓Experience 3: Work location: Cardiff, UK
  • ✓Experience 3: Employment dates: May 2021 - Aug 2022
  • ✓Experience 3: Description or achievements: Present
  • ✓Experience 4: Job title: Junior Machine Learning Engineer
  • ✓Experience 4: Company name: Harbourlight Data Services
  • ✓Experience 4: Work location: Exeter, UK
  • ✓Experience 4: Employment dates: Jun 2020 - Apr 2021
  • ✓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 Stewart
07700 90XXXX
toby.stewart@example.com
tobystewart
Bristol, BS1 4DJ
SUMMARY
AI Research Engineer with more than six years of experience developing machine learning systems, including LLMs, agentic AI and financial crime compliance. Develop models from evaluation through production, combining foundation model fine-tuning, prompt engineering and human feedback to improve AML workflows. Partner with engineering and product teams to assess model performance, guide feature delivery and apply computer vision to financial documents. Bring a research-led approach to practical model development, benchmarking and MLOps.
EXPERIENCE
AI Research Engineer
Jan 2024 - Present
Cedarstone Analytics
Bristol, UK
Developed LLM-based workflows for financial crime compliance, helping financial institution clients review complex transaction alerts and AML case material with traceable model outputs.
Fine-tuned foundation models for specialist compliance tasks and used prompt engineering to improve extraction of relevant entities and risk indicators from varied documents.
Designed agentic AI systems that combine retrieval, tool use and controlled hand-offs, then tested their behaviour against representative compliance scenarios.
Built benchmarks and evaluation pipelines covering factual accuracy, case completeness and consistency; used back-testing against historical cases to guide model changes before release.
Established continuous improvement loops that incorporated human feedback from compliance analysts into model iteration and evaluation.
Introduced MLOps practices for versioning datasets and models, monitoring deployed systems and documenting release decisions with engineering and product colleagues.
Prototyped a computer vision model to extract information from scanned identity and financial documents, assessing performance across document types before integration planning.
Optimised an alert-review workflow by profiling model latency and batching document extraction, reducing median case processing time from 11 minutes to 8 minutes.
Partnered with engineers to release an analyst review feature with traceable model outputs and approval controls, reducing manual evidence collation by 18% across a pilot.
Machine Learning Research Engineer
Sep 2022 - Dec 2023
North Quay Intelligence
Bath, UK
Built machine learning prototypes for document triage and operational risk analysis, translating product questions into testable model objectives with software engineers and product managers.
From 2023, evaluated LLMs for structured information extraction, comparing prompt designs and fine-tuning approaches on a reviewed sample of compliance documents.
Created a benchmark and back-testing process that surfaced failure patterns across document classes and gave product teams a consistent basis for model selection.
Improved a computer vision pipeline for scanned forms by refining image pre-processing and evaluating classification results against a labelled test set.
Packaged repeatable training and deployment workflows, adding experiment tracking and model monitoring to strengthen MLOps practice.
Built an evaluation set from analyst-reviewed compliance documents and added error labels by document class, helping product teams compare model changes before release.
Machine Learning Engineer
May 2021 - Aug 2022
Lattice Risk Labs
Cardiff, UK
Trained custom machine learning models to prioritise unusual transaction activity for analyst review, balancing detection quality with the need for explainable operational outcomes.
Developed image classification and document-processing experiments for financial records, documenting dataset limitations and evaluation results for engineering stakeholders.
Built reproducible data preparation and model evaluation workflows, using error analysis to distinguish model issues from inconsistent source data.
Worked with product colleagues to turn analyst feedback into revised model requirements and focused test cases.
Junior Machine Learning Engineer
Jun 2020 - Apr 2021
Harbourlight Data Services
Exeter, UK
Prepared transaction and customer data for supervised modelling, applying validation checks that reduced avoidable data errors in research datasets.
Compared baseline classification approaches for financial risk use cases and presented performance trade-offs to senior researchers.
Documented model experiments and supported repeatable evaluation, building a sound foundation in machine learning research and engineering practice.
LEADERSHIP, AWARDS & RECOGNITION
Recognised by the Cedarstone Analytics product group in 2025 for clear evaluation guidance during the launch of a compliance model feature.
Mentored two junior colleagues at North Quay Intelligence on experiment documentation and model evaluation practices.
EDUCATION
MSc Artificial Intelligence
2019
University of Nottingham
GPA: 3.8
Nottingham, UK
CERTIFICATIONS
Generative AI with Large Language Models, DeepLearning.AI and Coursera
2024
Machine Learning Engineering for Production (MLOps) Specialisation, DeepLearning.AI and Coursera
2023
TECHNICAL SKILLS
Programming Languages: Python, SQL, R
Machine Learning Frameworks: PyTorch, TensorFlow, scikit-learn
LLM Platforms: Hugging Face Transformers, OpenAI API, Anthropic API
Computer Vision Libraries: OpenCV, Pillow, torchvision
Data Processing: pandas, NumPy, Apache Spark
Experiment Tracking: MLflow, Weights & Biases, TensorBoard
Cloud Platforms: AWS, Microsoft Azure, Google Cloud
Data Storage: PostgreSQL, MongoDB, Amazon S3
Development Tools: Git, Docker, Kubernetes
API Technologies: FastAPI, Flask, REST
SKILLS
AI research
Machine learning
LLMs
Agentic AI
Prompt engineering
Model fine-tuning
Computer vision
Financial crime compliance
Anti-money laundering
Model evaluation
Benchmarking
Back-testing
MLOps
Human feedback loops
Product collaboration
PROFESSIONAL AFFILIATIONS
Member, The Alan Turing Institute community, engaging with its data science and artificial intelligence professional network.
Member, British Computer Society, participating in its professional computing community.

Matched keywords (18)

  • machine learningOriginal keyword in JD: 3+ years' experience in AI research OR machine learning OR AI engineering
  • LLMsOriginal keyword in JD: Large language models (LLMs)
  • agentic AI systems
  • foundation model fine-tuning
  • prompt engineering
  • financial crime compliance
  • Anti-money launderingOriginal keyword in JD: Anti-money laundering (AML)
  • model evaluation
  • benchmarking
  • back-testing
  • evaluation pipelines
  • MLOps
  • Trained custom machine learning modelsOriginal keyword in JD: Custom AI model training
  • Human feedback loops
  • Established continuous improvement loops that incorporated human feedback from compliance analysts into model iteration and evaluation.Original keyword in JD: Iterative model development
  • computer vision
  • Optimised an alert-review workflow by profiling model latency and batching document extraction, reducing median case processing time from 11 minutes to 8 minutes.Original keyword in JD: AI system optimisation
  • Partnered with engineers to release an analyst review feature with traceable model outputs and approval controlsOriginal keyword in JD: AI-driven feature delivery

Missing keywords (2)

  • Engineering and product collaboration
  • Emerging AI technologies
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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