Teams at HighLevel

Leads QA strategy and quality initiatives for AI products, architecting scalable automation frameworks and developing testing methodologies for RAG, hallucination, drift, and bias. Owns the AI quality charter and roadmap, influences architecture and cross-functional teams, coaches QA staff, and drives reliability and risk reduction. Bonus responsibilities include CI/CD, cloud platforms, MLOps monitoring, and AI workload performance testing.

Leads performance and scalability testing for distributed, production-scale systems. Builds shared load-testing frameworks, workload models, production-like Kubernetes and cloud environments, and automated CI/CD regression checks. Conducts load, stress, soak, and spike testing; performs capacity planning and root-cause analysis across APIs, databases, queues, and frontends. Establishes performance targets, interprets observability data, influences engineering decisions, and mentors junior team members.

Lead quality engineering vision and roadmap across product lines, drive CI/CD and release readiness, embed shift-left/shift-right practices, adopt AI-driven testing, mentor SDET managers, align quality metrics with executives, and architect test strategies for distributed systems and mobile.