Director Engineering - Automation, Performance and AI Quality

NICE•Sandy, UT

About The Position

NiCE is seeking a visionary engineering leader to architect and lead its quality engineering discipline, with ownership of automation infrastructure, performance engineering, and AI-driven quality initiatives. The Director will build and mentor teams of engineers and architects and establish how modern software is validated at scale. The role focuses on disrupting legacy testing approaches, championing agentic test generation, and integrating AI-quality practices throughout the SDLC, from the first commit through production monitoring. It serves as an organizational bridge between engineering velocity and product reliability. The position leads AI-augmented quality initiatives, including LLM-powered test generation, triage, and root-cause analysis; frameworks for validating AI/ML outputs and non-deterministic behavior; evaluation and integration of emerging AI quality tools; and responsible AI testing practices, coverage standards, and governance. It owns automation strategy across UI, API, and integration layers, scalable test frameworks, shift-left testing in CI/CD, coverage KPIs, flakiness SLAs, automation ROI metrics, and AI-assisted test maintenance. The Director also leads enterprise-scale performance and load testing, defines reliability objectives with SRE, establishes continuous performance validation, directs analysis of regressions and bottlenecks, and oversees performance and APM toolchains. Responsibilities include leading and growing automation and performance engineering teams and architects, setting technical direction and organizational objectives, hiring senior talent, building succession depth, fostering continuous learning and ownership, and partnering with Product, DevOps, and R&D leadership.

Requirements

12+ years in software engineering or QA.
5+ years in engineering leadership at the Director level or senior Staff+ with direct reports.
Deep hands-on background in test automation architecture, including building frameworks from scratch.
Proven track record leading performance engineering at scale, including 10,000+ concurrent users, distributed systems, and cloud-native environments.
Experience shipping or operating AI/ML systems and understanding their quality challenges.
Prior success managing multi-team organizations with individual-contributor engineers and architects, totaling 10–40+ people.
Working knowledge of LLM capabilities and limitations relevant to software testing, including prompt engineering, RAG, and agentic workflows.
Experience evaluating or integrating AI coding assistants such as GitHub Copilot, Claude, Cursor, or equivalents in engineering workflows.
Comfort with statistical testing concepts, including model drift detection, data validation, and behavioral testing for non-deterministic outputs.
Proficiency in at least one scripting or programming language: Python, TypeScript, or Java.
Proficiency with modern CI/CD tooling.
Exceptional communication skills, including translating technical concepts into executive-level strategy and roadmaps.
Ability to influence organizational culture and engineering standards beyond the direct organization.
Data-driven decision-making focused on measuring quality outcomes.
Ability and inclination to hire and develop senior talent.
Comfort operating in ambiguity and driving clarity in fast-moving environments.

Nice To Haves

Enterprise software domain experience, such as SaaS, CCaaS, fintech, or similar high-uptime or regulated environments.
Familiarity with observability and APM tools such as Datadog, Grafana, Dynatrace, or New Relic.

Responsibilities

Define and lead AI-augmented quality initiatives across the organization.
Deploy LLM-powered test generation, triage, and root-cause analysis tooling.
Build frameworks for validating AI/ML model outputs and non-deterministic behaviors.
Evaluate and integrate emerging AI quality toolchains, including GitHub Copilot, Claude, and agents.
Champion responsible AI testing practices, coverage standards, and governance models.
Own automation strategy across UI, API, and integration layers.
Architect scalable, maintainable test frameworks.
Drive shift-left testing embedded in CI/CD pipelines.
Establish coverage KPIs, flakiness SLAs, and automation ROI metrics.
Lead adoption of self-healing and AI-assisted test maintenance.
Lead performance and load testing strategy at enterprise scale.
Define SLOs/SLAs and partner with SRE on reliability thresholds.
Design continuous performance validation integrated into delivery pipelines.
Drive root-cause analysis for regressions, bottlenecks, and latency spikes.
Oversee performance toolchains and APM integrations.
Lead and grow teams of automation engineers, performance engineers, and architects.
Set technical direction, OKRs, and career ladders for the quality engineering organization.
Hire senior individual contributors and architect-level talent, and build succession depth.
Foster a culture of ownership, engineering craft, and continuous learning.
Partner cross-functionally with Product, DevOps, and R&D leadership.