Director, Quality Engineering & AI Testing

We are seeking a highly experienced Director of Quality Engineering & AI Testing to lead our global quality organization and establish world-class testing practices for enterprise SaaS and AI-driven products.

This leader will be responsible for building quality strategy, scaling test automation, improving release confidence, and driving reliability across platform, AI, and customer-facing solutions. The ideal candidate combines deep expertise in software quality engineering with modern approaches to AI testing, automation, and continuous delivery.

This role will partner closely with Engineering, Product Management, Applied AI, Customer Success, and Delivery teams to ensure Netomi delivers highly reliable, scalable, and secure enterprise-grade solutions.

• Define and execute the company-wide quality engineering strategy.

• Build and scale a high-performing global QA and Quality Engineering organization.

• Establish quality standards, governance, metrics, and release readiness frameworks.

• Drive a quality-first culture across Product, Engineering, and Delivery teams.

• Lead the design and implementation of scalable automated testing frameworks.

• Increase automation coverage across UI, API, integration, regression, and performance testing.

• Define best practices for CI/CD quality gates and release validation.

• Partner with Engineering to embed quality throughout the software development lifecycle.

• Develop testing frameworks for AI-powered agents, workflows, prompts, and LLM-driven experiences.

• Establish methodologies for evaluating accuracy, hallucinations, guardrails, safety, and reliability.

• Drive automated evaluation strategies for AI agent behavior and customer outcomes.

• Collaborate with Applied AI teams on model validation and production monitoring.

• Ensure quality across APIs, integrations, workflows, web applications, and enterprise deployments.

• Lead performance, scalability, reliability, and security testing initiatives.

• Improve defect prevention and root-cause analysis processes.

• Establish production quality monitoring and feedback loops.

• Define KPIs around release quality, defect leakage, automation coverage, customer-impacting incidents, and platform reliability.

• Analyze trends and implement continuous improvement initiatives.

• Drive operational excellence through data-driven decision-making.

• Recruit, mentor, and develop QA managers, automation engineers, and quality engineers.

• Build career frameworks and competency models for the quality organization.

• Foster a culture of ownership, innovation, and continuous learning.