Quality Engineering & AI Validation Manager

This is a remote position.Quality Engineering & AI Validation ManagerReporting to Head of Engineering, leads Accordion's centralized Quality Engineering and AI Validation function across software delivery pods. Owns the quality operating model, release-readiness standards, test strategy, AI validation framework, and quality metrics used to support both conventional software and AI-enabled solutions. Ensures quality is designed into delivery from the start, not inspected in at the end.Key ResponsibilitiesQuality Strategy & GovernanceDefine 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. AI Validation LeadershipBuild 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. Team LeadershipLead 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. Finance & Risk AlignmentPartner 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. Production QualityDefine 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. RequirementsRequired Qualifications8+ 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.You AreStructured, pragmatic, and highly credible. Comfortable asking tough questions about readiness, risk, and evidence. A builder of quality systems and operating models. Fluent in both engineering quality and business impact. Focused on trust, traceability, and scalable execution. BenefitsSalary plus a performance-based bonusActual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to skill set, years and depth of experience, education, certifications, cost of labor, and internal equity. Originally posted on Himalayas