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Requisitos e responsabilidades
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Responsibilities:
- 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.
Quality Strategy & Leadership:
- 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.
Test Automation & Engineering Excellence:
- 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.
AI & Agentic Testing:
- 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.
Enterprise Platform Quality:
- 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.
Metrics & Continuous Improvement:
- 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.
Team Leadership:
- 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.
Required Experience:
- 12+ years of experience in Software Quality Assurance, Quality Engineering, or Testing.
- 5+ years of leadership experience managing global QA organizations.
- Experience leading quality functions within enterprise SaaS companies.
- Strong background in test automation, CI/CD, and modern engineering practices.
- Experience testing distributed systems, APIs, cloud-native applications, and enterprise integrations.
- Proven track record of scaling quality organizations in high-growth environments.
Details
- 12+ years of experience in Software Quality Assurance, Quality Engineering, or Testing.
- 5+ years of leadership experience managing global QA organizations.
- Experience leading quality functions within enterprise SaaS companies.
- Strong background in test automation, CI/CD, and modern engineering practices.
- Experience testing distributed systems, APIs, cloud-native applications, and enterprise integrations.
- Proven track record of scaling quality organizations in high-growth environments.
- Significantly improve automation coverage across the platform.
- Reduce escaped defects and production incidents.
- Establish a scalable AI testing and evaluation framework.
- Improve release velocity while maintaining high quality standards.
- Build a world-class Quality Engineering organization aligned with Netomi's growth objectives.
Preferred Experience:
- Experience with AI, Generative AI, Conversational AI, Agentic AI, or Machine Learning products.
- Experience designing testing strategies for LLM-based applications.
- Familiarity with prompt evaluation, model validation, and AI quality metrics.
- Experience working with enterprise customers and large-scale deployments.
Technical Expertise:
- Test Automation Frameworks
- API Testing
- Performance & Load Testing
- CI/CD Pipelines
- Cloud Platforms (AWS preferred)
- Microservices Architecture
- AI/LLM Testing Methodologies
- Security & Reliability Testing
Success Metrics:
- Significantly improve automation coverage across the platform.
- Reduce escaped defects and production incidents.
- Establish a scalable AI testing and evaluation framework.
- Improve release velocity while maintaining high quality standards.
- Build a world-class Quality Engineering organization aligned with Netomi's growth objectives.
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FocoHead Of QAÁrea da vagaSinal de senioridadeLeadNível do candidatoStackAWS, CI/CD, LLMSkills principaisLocalização1 país aceitoElegibilidade Stack
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