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Amazon
Alexa is the voice activated digital assistant powering devices like Amazon Echo devices and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa.
We are seeking a Risk Specialist to join our Risk team, focused on maintaining the quality, accuracy, and integrity of AI-driven initiatives and risk management operations. In this role, you will work on essential Program and operations that support the development and continuous improvement of AI systems — including creating, reviewing, and managing datasets, monitoring compliance, and contributing to the mitigation of risks across products and services.
You will be responsible for executing and delivering risk projects within a well-defined risk framework, applying proven risk management methodologies to identify and address risks across areas such as regulatory, reputational, safety, policy, and business continuity. You will collaborate closely with your team and cross-functional stakeholders to drive meaningful, measurable outcomes.
This is an excellent opportunity for a detail-oriented professional who communicates clearly, thrives in a structured environment, and is eager to grow their expertise in risk management and AI operations within a collaborative, fast-paced environment.
Key job responsibilities
• Identify, define, and document risk pathways and threat scenarios; develop and maintain structured frameworks categorizing risks by type, severity, and likelihood
• Collaborate with stakeholders to keep risk frameworks aligned with current and emerging risk landscapes, using them to guide assessment, testing, and mitigation
• Monitor AI-driven workflows and automated decision-making pipelines for quality, accuracy, and compliance; identify gaps, escalate issues, and coordinate corrective actions with technical teams
• Triage and escalate vulnerabilities detected through monitoring, audits, or team submissions; assess severity, determine escalation paths, and drive timely resolution with cross-functional teams
• Own creation, maintenance, and QA of reference datasets for model evaluation and performance calibration; enforce standards for composition, labeling accuracy, and version control
• Design and oversee human review processes at critical AI decision points; define review criteria, sampling strategies, and feedback mechanisms to maintain output quality
• Act as liaison between your team and cross-functional partners; facilitate alignment on risk priorities, policy requirements, and project goals while managing expectations proactively
• Coach and support team members in risk assessment, data review, and process execution; lead calibration sessions to ensure consistency and identify skill gaps
• Oversee annotation and labeling quality through audits, spot-checks, and structured feedback; track quality metrics and implement corrective measures
• Own operational guidelines, annotation standards, and policy documentation; regularly review and update to reflect current policies, edge cases, and lessons learned
• Monitor team output against volume targets and SLAs; identify risks to attainment and develop proactive mitigation plans
• Manage intake, triage, and resolution of escalations; maintain clear records of decisions and outcomes
• Design and deliver onboarding programs and training materials for new team members; continuously improve content based on feedback and performance gaps
• Continuously evaluate workflows and processes to identify inefficiencies; implement improvements that enhance effectiveness, reduce errors, and improve scalability- 2+ years of data-driven business operations processes experience
- Bachelor's degree or equivalent
- Experience communicating clearly and concisely with leadership, stakeholders, and cross-functional teams
- Experience in compliance, audit or risk management
- Ability to recognize operational and risk management challenges and determine how AI-based solutions can be leveraged to drive efficiency, accuracy, and scalability.
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