Manager, Quality Engineering & AI Validation

Accordion•Boston, MA

15h•Hybrid

About The Position

The Manager of Quality Engineering and AI Validation leads Accordion's centralized Quality Engineering and AI Validation function across software delivery pods. This impactful role will be critical in owning 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. Above all, this leader will ensure quality is designed into delivery from the start, not inspected in at the end. This position will ideally be based in one of our US offices and is a hybrid role with the flexibility to work remotely 2 days per week (also eligible for remote candidates).

Requirements

8+ years of QA, software testing, or quality engineering experience, including team leadership – you are a builder of quality systems and operating models
Deep fluency in both engineering quality and business impact
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
Comfortability asking tough questions about readiness, risk, and evidence
Strong communication skills and credibility with Engineering, Product, and business stakeholders – you are structured, pragmatic, and highly credible
Consistent focus on trust, traceability, and scalable execution
Bachelor's degree preferred

Nice To Haves

Excited to be part of a growing team, with a focus on driving future growth
Passionate about delivering exceptional client service
Knowledgeable about the relationship between Private Equity sponsors and their portfolio companies, particularly within the office of the CFO
Willing to get your hands dirty in the details of a project while simultaneously seeing the whole picture
Comfortable managing projects with multiple complex workstreams while also focused on your single workstream (you are managing and doing)
A self-starter with a strong work ethic
Full of entrepreneurial spirit and comfortable in a fluid, flat organization
Able to effectively communicate complex issues and solutions, and raise issues to senior team members when necessary
A team player, able to work with team members across all levels
Someone who enjoys mentoring others and doing meaningful work
A leader of others; you lead by example

Responsibilities

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