AI Work Central

Risk Management Expert

SME CareersWritingFirst seen 9 Oct 2026

up to$90/hr as listed by SME Careers, USDEstimate a week at this rate
Apply on SME Careers

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Where
17 countries

United States, Germany, Brazil, India, Bangladesh, Bhutan, Indonesia, Cambodia, Sri Lanka, Malaysia, Nepal, Philippines, Pakistan, Singapore, Thailand, Timor Leste, Vietnam

Language
English
Type
Contract, remote
Field
Writing

Posted on SME Careers First seen 9 Oct 2026Checked 12 Oct 2026

From the SME Careers listing

In this hourly, remote contractor role, you will work as a Risk Management Subject Matter Expert (SME) to review AI-generated risk management responses and/or create expert risk content, evaluating reasoning quality and step-by-step decision-making while providing precise written feedback. You will assess solutions for accuracy, clarity, completeness, and adherence to the prompt; identify errors in risk identification, assessment methodology, controls design, mitigation planning, governance, and incident response logic; fact-check risk and compliance concepts; write high-quality explanations and model solutions that demonstrate correct approaches; and rate and compare multiple responses based on correctness and reasoning quality. This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs.

Important:

There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Responsibilities

  • Develop AI Training Content: Create detailed prompts and gold-standard outputs across risk topics (risk assessments, control mappings, mitigation plans, risk communications).
  • Optimize AI Performance: Evaluate and rank AI responses to improve correctness, rigor, clarity, and practical usefulness.
  • Ensure Model Integrity: Test AI outputs for inaccuracies, unsafe recommendations, misleading certainty, and bias; validate reliability across scenarios.

Requirements

  • Bachelor’s degree (or higher) in Business, Finance, Economics, Risk Management, Law, IT, or a related field (or equivalent professional experience).
  • 5+ years of professional experience in risk management (ERM, operational risk, compliance, internal audit, security risk, third-party risk, or similar).
  • Strong command of risk fundamentals: risk taxonomy, inherent vs residual risk, likelihood/impact scoring, control design and effectiveness, KRIs/KPIs.
  • Familiarity with risk frameworks and standards (as applicable): COSO ERM, ISO 31000, NIST (for security risk), SOC 2/ISO 27001 concepts, SOX controls awareness.
  • Experience with governance processes: risk registers, policy development, risk committees, approvals/escalation, and executive reporting.
  • Ability to evaluate scenarios for internal consistency (scope, assumptions, risk appetite, control gaps, remediation feasibility, and timelines).
  • Strong written communication: ability to translate complex risk topics into clear, decision-ready summaries; minimum C1 English proficiency.
  • Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
  • Professional certifications (e.g., CRISC, CISA, CIA, ISO 27001 LI/LA, PMP, FRM) are a plus.
  • Prior experience with AI data training/annotation, policy review, audit/risk QA, or regulatory writing is strongly preferred.

Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.

Prepare for the assessment

Practice questions and what each platform says about its screening for this kind of role:

Terms in this listing

  • Prompt: The input given to a model: a question, an instruction or a conversation so far.
  • Assessment: Any screening step a platform uses before giving you work: a quiz, a skills test, a language test, a writing sample or an AI interview.
  • Taxonomy: The fixed list of categories a project uses for labeling, often with definitions and examples for each.
  • Annotation: Adding labels or notes to data such as text, images, audio or video so a model can learn from it.

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