AI Quality Test Automation - Lead Software Engineer

<div class="content-intro"><p>At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.</p></div><p><strong>So, what’s the role all about?</strong></p> <p>As Lead Software Engineer – AI Quality &amp; Test Automation, you own the strategy and execution of a modern, AI-accelerated test automation program. You build and run the platforms, frameworks, and CI quality gates that keep releases reliable at high velocity — using AI to scale test creation, maintenance, and triage across teams. You don’t retrofit testing onto AI workflows; you engineer quality into them from the ground up, ensuring every stage of an AI-assisted pipeline is observable, trustworthy, and continuously improving.</p> <p><strong>How will you make an impact?&nbsp;&nbsp;</strong></p> <p><strong>Platform &amp; Automation Leadership</strong></p> <ul> <li>Own the automation platform: define and build scalable frameworks, standards, and governance (services, APIs, UI) that enable teams to produce robust, consistent coverage</li> <li>Set direction for test automation tooling and AI-assisted techniques that accelerate test design, authoring, maintenance, and triage</li> <li>Lead by example — write automation, set the quality bar, mentor engineers, and drive adoption of the practices you establish</li> <li>Own CI automation health: build and maintain test environments, pipelines, and tooling (including containers/CI), reduce flakiness, and shorten feedback loops</li> <li>Be a highly visible advocate for quality — lead cross-team discussions, drive alignment and decisions, and escalate risks and blockers immediately</li> </ul> <p><strong>AI-Native Test Engineering</strong></p> <ul> <li>Lead the adoption of LLM-powered test generation: from natural language requirement ingestion to executable, maintainable test output</li> <li>Build and maintain a self-healing test infrastructure layer — leveraging AI to detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously</li> <li>Define prompt engineering standards, context injection patterns, and RAG architectures that ground test generation in real codebase context</li> <li>Implement guardrails to ensure AI-generated test output is verified, traceable, and safe to ship — including versioning, ownership attribution, and confidence scoring</li> <li>Own automated coverage and risk reporting (unit/integration/e2e) and use it to drive targeted gap closure and release readiness</li> </ul>