Quality Engineering & AI Validation Manager

This is a remote position.

Reporting to Head of Engineering leads Accordions 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.

Bachelors 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