Quality Engineering & AI Validation Manager

Reporting 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 Responsibilities

Quality Strategy & Governance

• 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.

AI Validation Leadership

• 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.

Team Leadership

• 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.

Finance & Risk Alignment

• 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.

Production Quality

• 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

Required Qualifications

• 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.

You Are

• Structured, 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.

Benefits