Quality Engineering & AI Validation Manager

<h3>This is a remote position.</h3><div><b>Quality Engineering &amp; AI Validation Manager</b><p>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.</p><h3><b>Key Responsibilities</b></h3><h3>Quality Strategy &amp; Governance</h3><ul><li>Define the firm-wide quality strategy, release gates, and validation standards across development pods.</li> <li>Establish a risk-based quality model for traditional software and AI-enabled workflows.</li> <li>Define when pod-level validation is sufficient and when independent QA validation is required.</li> </ul><h3>AI Validation Leadership</h3><ul><li>Build and operationalize the QA approach for AI-led products, including benchmark datasets, scoring rubrics, regression comparisons, and grounded-output validation.</li> <li>Establish testing expectations for instruction adherence, consistency, business correctness, control compliance, and hallucination risk.</li> <li>Partner with Engineering and Product leaders on quality implications of prompt, model, workflow, and tooling changes.</li> </ul><h3>Team Leadership</h3><ul><li>Lead and develop quality engineers, AI quality analysts, and domain-oriented QA resources.</li> <li>Improve the maturity of automation, evaluation routines, test evidence standards, and release discipline.</li> <li>Create a scalable central model that supports pods without becoming a bottleneck.</li> </ul><h3>Finance &amp; Risk Alignment</h3><ul><li>Partner with Finance and practice SMEs to ensure solutions are validated against real business use, materiality, and control expectations.</li> <li>Ensure high-risk workflows receive the right level of domain review before production release.</li> </ul><h3>Production Quality</h3><ul><li>Define the framework for production quality monitoring, including escaped defects, output-quality degradation, reviewer overrides, and control failures.</li> <li>Create visibility into quality trends, readiness, and recurring failure patterns for Engineering and leadership.</li> </ul><br><h3>Requirements</h3><h3><b>Required Qualifications</b><ul><li>8+ years of QA, software testing, or quality engineering experience, including team leadership.</li> <li>Experience building test strategy, release-readiness processes, and automation programs in modern software environments.</li> <li>Experience supporting AI-enabled applications, workflow automation, data products, or decision-support systems.</li> <li>Strong understanding of functional, integration, regression, API, and data validation approaches.</li> <li>Ability to translate business-critical finance workflows into quality controls and release criteria.</li> <li>Strong communication skills and credibility with Engineering, Product, and business stakeholders.</li> </ul></h3><h3>·Bachelor's degree preferred.</h3><h3>You Are<ul><li>Structured, pragmatic, and highly credible.</li> <li>Comfortable asking tough questions about readiness, risk, and evidence.</li> <li>A builder of quality systems and operating models.</li> <li>Fluent in both engineering quality and business impact.</li> <li>Focused on trust, traceability, and scalable execution.</li> </ul><br></h3><h3>Benefits</h3><div>Salary plus a performance-based bonus<div>Actual 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.<br></div></div></div><p>Originally posted on <a href="https://himalayas.app">Himalayas</a></p>