Head of QA

Responsibilities:

• Quality Strategy and Leadership -
• Define and execute the Quality Engineering vision and roadmap aligned with organizational goals.
• Build and lead a multi-disciplinary QA team (Platform QA, Data Quality, Automation, and Performance Engineering).
• Partner with engineering, product, and customer success leaders to ensure quality is embedded across the SDLC.
• Establish quality KPIs and dashboards for visibility into release readiness, product stability, and production health.


• End-to-End Quality Ownership -
• Own functional and regression testing strategy across microservices, SDKs, APIs, and UI layers.
• Drive non-functional testing excellence — performance, scalability, reliability, failover, and compatibility.
• Lead security testing initiatives, collaborating with DevSecOps and compliance teams to ensure adherence to ISO, SOC 2, and GDPR standards.
• Introduce AI-augmented testing frameworks leveraging machine learning for predictive defect detection and data drift analysis.


• Data Science and AI Quality -
• Partner with the Data Science org to establish data quality pipelines for training and inference data.
• Define testing standards for AI model accuracy, precision, bias, and drift monitoring.
• Ensure data-driven validation frameworks are integrated into MLOps and deployment cycles.


• Customer Success Engineering Quality -
• Oversee QA processes for custom deployments, integrations, and client-specific configurations.
• Build automation frameworks to validate end-to-end customer experience workflows and integrations (Genesys, Salesforce, Zendesk, etc.).


• Process and Tooling Excellence -
• Institutionalize CI/CD-driven test automation for faster and safer deployments.
• Evaluate and implement next-gen QA tools for test management, performance benchmarking, and observability.
• Drive shift-left testing culture and early defect detection through static analysis, contract testing, and chaos testing.
• Foster collaboration between QA, DevOps, and Observability teams for quality-in-production metrics.

Requirements:

• 10+ years of experience in software quality engineering, with 3+ years leading QA teams in a SaaS or platform environment.
• Proven experience in AI/ML or data-centric product quality, including validation of data pipelines and ML models.
• Deep understanding of microservices architecture, cloud platforms (AWS/Azure), and CI/CD pipelines.
• Hands-on experience with automation frameworks (Selenium, Cypress, Playwright, TestNG, PyTest, etc.).
• Expertise in performance testing tools (JMeter, Gatling, Locust) and security testing (OWASP, Burp Suite, ZAP).
• Strong grasp of API and SDK testing, ideally for multi-platform environments (web, mobile, voice).
• Exceptional communication, leadership, and stakeholder management skills with an ability to influence across functions.


• Preferred Skills
• Experience in AI/ML testing, data validation, and model governance frameworks.
• Familiarity with observability stacks (ELK, Grafana, Datadog, OpenTelemetry) for production monitoring.
• Understanding of GenAI systems, prompt testing, and retrieval-augmented generation (RAG) validation.
• Exposure to B2B enterprise platforms and multi-tenant SaaS architectures.
• Knowledge of secure SDLC and compliance frameworks (SOC 2, ISO 27001).

Why Netomi

• Work at the frontier of AI-driven customer experience.
• Influence quality strategy across platform, AI, and customer success ecosystems.
• Collaborate with top-tier engineers and data scientists in a high-growth, innovation-driven culture.
• Competitive compensation, fast career progression, and global exposure.