Financial Crime & AML Compliance Expert (Remote Contract)

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow careers teaching AI by consolidating projects, building professional profiles, and making specialized work easier to track and apply for.

Creating an OpenTrain account is free — the platform is also the hiring and contracting organization for this role.

About AI Training Work

AI training is the human side of building artificial intelligence: people design examples, review model outputs, and create rules that help machine learning systems behave correctly. Contributors do work like scenario design, evaluation/rating, and documentation that directly shapes how models detect and respond to fraud and abuse.

This role focuses on text-based tasks (scenario creation and evaluation) used to train and benchmark generative models and detection systems.

The Role

OpenTrain is hiring a Financial Crime & AML Compliance Expert to create and review realistic fraud, scam, money laundering, and abuse scenarios and to build benchmark criteria and escalation frameworks for AI training projects.

This is a remote contractor, part-time role (20+ hours/week). Tasks are text-focused and include both text generation and evaluation/rating work. The role is worldwide and requires English proficiency.

• Data type: TEXT; Label tasks: TEXT_GENERATION and EVALUATION_RATING.
• Employment: Contractor, Part-time; Time commitment: 20+ hours/week; Languages: English; Worldwide applicants welcome.

What You’ll Do

You will translate real-world financial crime experience into training materials, detection criteria, and practical guidance that improve AI accuracy and operational relevance.

• Design and document fraud, scam, money laundering, and marketplace abuse scenarios for model training.
• Define social-engineering red flags, typologies, and indicators of compromise used for detection rules.
• Create escalation grading scales and benchmark criteria for detection frameworks and triage workflows.
• Review and provide detailed, actionable feedback on guidance, workflows, and deliverables to improve practical relevance.
• Write clear, operational rulebooks, scenario libraries, and examples for both technical and non-technical audiences.

Requirements

Applicants must have recent, hands-on experience in financial crime work and the ability to communicate complex risk concepts clearly.

• Recent hands-on experience in fraud investigations, AML compliance, payments risk management, or related financial crime roles.
• Strong knowledge of scams, phishing, synthetic identity fraud, marketplace abuse, and money laundering typologies.
• Experience building scenario libraries, detection rulebooks, or training materials.
• Clear written and verbal communication for both technical and non-technical audiences.
• Analytical rigor and curiosity to assess evolving financial crime typologies and adapt controls.
• Advanced degree or professional certification such as CAMS, CFE, or CFCS is preferred but not required.

Who Should Apply

This opportunity fits people who currently work or recently worked in fraud/AML operations and who want flexible, remote project work that directly shapes how AI detects and responds to financial crime.

The posted experience level is entry level for OpenTrain projects, but applicants should possess real-world operational knowledge as listed in the requirements.

• You prefer part-time, remote contract work and can commit 20+ hours/week.
• You can document examples and explain risk severity, escalation criteria, and detection approaches clearly.

Compensation, Logistics & How It Works

Pay is hourly: $50–$90 USD per hour (projects are billed PAY_PER_HOUR). Work is performed as a contractor on a part-time basis.

OpenTrain handles hiring and contracting. Create a free OpenTrain account to apply, build your profile, and submit work. As the hiring organization, OpenTrain will provide onboarding materials, task instructions, and quality guidelines for assigned projects.