Senior Quality Engineer, Data & AI Platform

com ) is a leading provider of hospitality-specific software solutions, delivering cloud-based tools for hotel accounting, financial reporting, labor management, payroll, and business intelligence. Built by hoteliers for hoteliers, M3 empowers hotel owners, operators, and management companies to streamline back-office operations, reduce costs, gain real-time insights, and drive portfolio performance across thousands of properties in North America and beyond. Description Summary: The Senior Quality Engineer - Data AI sits at the intersection of data engineering, machine learning product delivery, and quality assurance.

This role is responsible for validating AI-powered and data-driven features across M3’s hospitality accounting platform, with primary ownership of quality for the Data AI team’s active products. Unlike traditional QE roles focused on deterministic software, this position requires quality thinking applied to systems that produce probabilistic outputs - where “passing” is defined by accuracy thresholds, not binary correctness. The right candidate understands how AI products fail differently and is motivated to build the testing practice that prevents those failures from reaching customers.

This is an individual contributor role with significant cross-functional responsibility, including collaboration with Engineering, Product, Data, and external contractor teams. Essential Duties: The duties listed below are the essential functions of this position, and they may change as the needs of the company demand. All associates are expected to do what is necessary to get the work done and to cooperate fully with their supervisor’s requests for additional or altered duties.

Design and execute test strategies for AI/ML-enabled features; define acceptance thresholds (precision, recall, F1) in partnership with Engineering and Product. Validate AI-generated outputs - mappings, anomaly flags, LLM summaries - against domain-grounded acceptance criteria; build regression suites to detect concept drift, label drift, and feature degradation. Evaluate LLM outputs for accuracy, hallucination, and format compliance; build prompt regression test suites to catch behavior changes when model versions or system prompts are updated.

Write data validation logic in Python or SQL against tables across Bronze, Silver, and Gold layers; design and automate data contract tests covering schema, null rates, referential integrity, and row counts. Implement automated validation checkpoints in Databricks pipelines; apply statistical testing methods to pipeline output validation. Define and instrument production monitoring checks for model performance: alert thresholds, confidence degradation, and data drift.

Perform functional, regression, integration, and exploratory testing across releases, enhancements, and defect fixes using manual and automated approaches. Design, develop, maintain, and execute automated test scripts using frameworks such as Katalon and Playwright; support CI/CD quality gates. Develop and execute test cases for ETL processes, data warehouse workflows, and API validation.

Review application logs and monitoring tools to identify and track defects; coordinate builds, deployments, and software migrations for planned releases and hotfixes. Maintain and utilize testing tools including Azure DevOps, SQL Server, Postman/Swagger/SoapUI, and automation frameworks. Participate in Agile ceremonies; collaborate with Product, Engineering, Data, and external contractor teams to ensure comprehensive coverage and aligned acceptance criteria.

Communicate quality risks early and in writing; develop and maintain a QE runbook for AI/ML products.

Requirements

associated with the assigned Technical Security Level. Other duties as assigned.