Back
Remote, mid-level QA role with broad/full-stack AI/data testing attracts many qualified applicants.
AI/data-focused QA requires domain-specific expertise, somewhat reducing cross-industry transferability.
Explicit 5+ years plus required QA, automation and CI/CD skills make filters moderately strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Architect and implement comprehensive testing strategies for data-intensive AI/ML products ensuring reliability and performance.
Design, develop, and deploy scalable automation-driven QA frameworks and CI/CD pipelines using GitHub Actions.
Conduct full-stack quality assurance from data pipelines through AI/ML inference to APIs and user interfaces within a lean, cross-functional team.
5+ years of hands-on Quality Assurance experience focused on data-intensive applications, AI/ML systems, or enterprise SaaS products.
Proven expertise in full-stack QA testing strategies including backend, data, and frontend components.
Experience in automation testing frameworks (e.g., Pytest, Selenium, Playwright) and building CI/CD pipelines using GitHub Actions.
Work Experience Required: 5+ years in Quality Assurance or related roles with data/AI focus.
Capable of independently designing innovative AI/ML testing methodologies addressing model bias, data drift, and LLM-specific challenges.
Experienced in operating within fast-paced, startup environments driving robust QA and DevSecOps practices.
Skilled at collaborating cross-functionally to embed quality and influence software delivery lifecycle in data-centric products.