Quality Engineering, AI Validation Manager

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🕒 April 9

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Shyft6

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2019

👥 HR Tech

🎯 Recruiter

🤝 B2B

HR Tech • Recruitment • B2B

Shyft6 is a trusted partner for innovative companies, specializing in workforce solutions that encompass staffing and recruiting, workforce management, and human capital strategy. With over 26 years of combined experience, Shyft6 offers a comprehensive range of services including contingent workforce supplier management, recruitment process outsourcing, staff augmentation, executive search, and multi-state payroll. Their focus is on empowering organizations through effective workforce development and project management, ensuring successful delivery and skilled talent acquisition.

📋 Description

• Define the firm-wide quality strategy, release gates, and validation standards across development pods. • Establish a risk-based quality model for traditional software and AI-enabled workflows. • Define when pod-level validation is sufficient and when independent QA validation is required. • Build and operationalize the QA approach for AI-led products, including benchmark datasets, scoring rubrics, regression comparisons, and grounded-output validation. • Establish testing expectations for instruction adherence, consistency, business correctness, control compliance, and hallucination risk. • Partner with Engineering and Product leaders on quality implications of prompt, model, workflow, and tooling changes. • Lead and develop quality engineers, AI quality analysts, and domain-oriented QA resources. • Improve the maturity of automation, evaluation routines, test evidence standards, and release discipline. • Create a scalable central model that supports pods without becoming a bottleneck. • Partner with Finance and practice SMEs to ensure solutions are validated against real business use, materiality, and control expectations. • Ensure high-risk workflows receive the right level of domain review before production release. • Define the framework for production quality monitoring, including escaped defects, output-quality degradation, reviewer overrides, and control failures. • Create visibility into quality trends, readiness, and recurring failure patterns for Engineering and leadership.

🎯 Requirements

• 8+ years of QA, software testing, or quality engineering experience, including team leadership. • Experience building test strategy, release-readiness processes, and automation programs in modern software environments. • Experience supporting AI-enabled applications, workflow automation, data products, or decision-support systems. • Strong understanding of functional, integration, regression, API, and data validation approaches. • Ability to translate business-critical finance workflows into quality controls and release criteria. • Strong communication skills and credibility with Engineering, Product, and business stakeholders. • Bachelor's degree preferred.

🏖️ Benefits

• Salary plus a performance-based bonus