Quality Assurance Engineer - Data
The Quality Assurance Engineer will ensure the quality and reliability of applications by planning and executing various testing methodologies while collaborating closely with development teams.
About Gramian
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
About the Role
We are seeking a detail-oriented QA Review Team Lead to oversee quality assurance activities for AI training data and ensure annotated outputs are accurate, consistent, and delivered on time. In this role, you will lead QA reviewers, manage audit and review queues, assess annotation quality, identify recurring errors, and provide clear, actionable feedback to improve team performance. You will also train new reviewers, clarify guidelines for annotators, and escalate complex or recurring issues to the Senior QA Lead. The ideal candidate combines strong quality assurance skills with experience mentoring team members and applying detailed guidelines in a fast-paced environment.
CONTRACT: Contractor assignment, 16 weeks
COMMITMENT: Hourly, up to 40h/week
LOCATIONS: Remote - Brazil, Colombia, India
Key Responsibilities
Requirements
3β5 years of overall professional experience, including at least 1 year of experience leading, mentoring, or supervising reviewers or team members.
Strong attention to detail and the ability to apply complex guidelines consistently.
Excellent written and verbal communication skills.
Ability to provide constructive, actionable feedback and explain quality expectations clearly.
Strong organizational skills and the ability to manage multiple review queues and competing deadlines.
Ability to identify patterns in errors and support corrective actions.
Availability to work the specified schedule for the full 16-week project.
The Quality Assurance Engineer will ensure the quality and reliability of applications by planning and executing various testing methodologies while collaborating closely with development teams.
Own end-to-end quality for an AI-driven risk platform by automating UI, API, data, and model testing, validating accurate and reproducible results, and maintaining audit-ready evidence.
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