AI Quality Test Automation - Lead Software Engineer

Nice; Atlanta (GA); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

NiCE-FLEX hybrid model

Office in Sandy, Utah

Job summary

NiCE in Atlanta is seeking a Lead Software Engineer – AI Quality & Test Automation to own strategy and execution of an AI-accelerated test program. You’ll build 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 will lead platform and automation leadership, drive AI-assisted test design, guardrails for AI output, and mentor engineers while collaborating with product and engineering

Qualifications

• Bachelor’s degree in Computer Science or related field, or equivalent practical experience.
• 10+ years of software engineering experience with emphasis on test automation and quality engineering practices.
• Strong programming skills in one or more languages: Python, TypeScript, Java, or C#.
• Experience building automation for APIs and distributed systems; UI automation experience is a plus.
• Experience with CI/CD, test reporting/observability, and maintaining reliable pipelines.
• Proven ability to apply AI/LLM tools to scale automation with guardrails so AI output is verified and safe to ship.
• Hands-on understanding of agentic AI patterns: tool use, multi-agent orchestration, planning loops, and human-in-the-loop design.
• Excellent debugging and root-cause analysis skills across code, data, and infrastructure.
• Strong communication skills; able to translate quality risks into clear tradeoffs and action plans.
• Demonstrated technical leadership across teams — driving standards, roadmap execution, and stakeholder alignment.

Responsibilities

• Lead platform & automation strategy and define scalable frameworks, standards, and governance.
• Own CI automation health: build and maintain test environments, pipelines, and tooling (including containers/CI).
• Lead cross‑team discussions and drive adoption of quality practices.
• Own AI‑Native test engineering and guardrails for verifiable AI output.
• Define prompt engineering standards and RAG architectures grounded in real codebase context.
• Build risk‑scoring models to adjust gate strictness based on changes and deployment context.
• Architect observability for automated pipelines to surface quality signals.
• Establish rollback and circuit‑breaker patterns for autonomous deployments.
• Lead risk‑based test strategy with product and engineering for high-velocity delivery.

Skills

Python

TypeScript

Java

C#

Test automation

CI/CD

Debugging

Distributed systems

LLM/AI tooling

Education

Bachelor’s degree in Computer Science or related field

Tools

pytest

JUnit

NUnit

Playwright

Cypress

Docker

Kubernetes

GitHub Actions

Jenkins

Azure DevOps

Job description

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.

What’s the role all about?

As Lead Software Engineer – AI Quality & 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.

How will you make an impact?

Platform & Automation Leadership

• Own the automation platform: define and build scalable frameworks, standards, and governance (services, APIs, UI) that enable teams to produce robust, consistent coverage
• Set direction for test automation tooling and AI‑assisted techniques that accelerate test design, authoring, maintenance, and triage
• Lead by example — write automation, set the quality bar, mentor engineers, and drive adoption of the practices you establish
• Own CI automation health: build and maintain test environments, pipelines, and tooling (including containers/CI), reduce flakiness, and shorten feedback loops
• Be a highly visible advocate for quality — lead cross‑team discussions, drive alignment and decisions, and elevate risks and blockers immediately

AI‑Native Test Engineering

• Lead the adoption of LLM‑powered test generation: from natural language requirement ingestion to executable, maintainable test output
• 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
• Define prompt engineering standards, context injection patterns, and RAG architectures that ground test generation in real codebase context
• Implement guardrails to ensure AI‑generated test output is verified, traceable, and safe to ship — including versioning, ownership attribution, and confidence scoring
• Own automated coverage and risk reporting (unit/integration/e2e) and use it to drive targeted gap closure and release readiness

Quality Gates & CI/CD

• Lead risk‑based test strategy with product and engineering — define acceptance criteria and quality gates that support high delivery velocity without sacrificing customer‑impacting quality
• Design adaptive quality gates for AI‑accelerated CI/CD pipelines — gates that reason about risk, not just pass/fail thresholds
• Build risk‑scoring models that adjust gate strictness based on change scope, code origin (human vs. AI‑generated), historical failure patterns, and deployment context
• Architect the observability layer for automated pipelines: surface signals that indicate poor quality decisions in real time
• Establish rollback and circuit‑breaker patterns for autonomous deployments triggered by quality signal degradation

AI Model & Agent Validation

• Build behavioral testing frameworks for validating AI agents and LLM‑powered features in production — testing non‑deterministic outputs with statistical rigor
• Design evaluation benchmarks for internal AI tooling: measuring task completion accuracy, hallucination rates, and decision quality over time
• Define adversarial and edge‑case testing methodologies for AI features: prompt injection resistance, boundary condition handling, and graceful degradation
• Partner with ML platform and data science teams to establish quality acceptance criteria for every model and agent promoted to production

Have you got what it takes?

• Bachelor’s degree in Computer Science or a related field, or equivalent practical experience.
• 10+ years of software engineering experience with a strong emphasis on test automation (unit, integration, end‑to‑end) and quality engineering practices
• Strong programming skills in one or more languages: Python, TypeScript, Java, or C#
• Platform mindset — energized by building infrastructure and frameworks that enable product teams to move faster
• Experience building automation for APIs and distributed systems; UI automation experience is a plus
• Experience with CI/CD, test reporting/observability, and maintaining reliable pipelines (e.g., GitHub Actions, Jenkins, Azure DevOps)
• Proven ability to apply AI/LLM tools to scale automation (e.g., generate/refine tests, expand edge cases, refactor brittle suites, accelerate failure triage) while implementing guardrails so AI output is verified, traceable, and safe to ship
• Hands‑on understanding of agentic AI patterns: tool use, multi‑agent orchestration, planning loops, and human‑in‑the‑loop design as applied to quality workflows
• Familiarity with LLM failure modes relevant to quality: hallucination, context loss, sycophancy, and over‑confident assertions
• Excellent debugging and root‑cause analysis skills across code, data, and infrastructure
• Strong communication skills; able to translate quality risks into clear tradeoffs and action plans
• Demonstrated technical leadership across teams — driving standards, roadmap execution, and stakeholder alignment
• Self‑directed, comfortable with ambiguity, and biased toward action

Desired Skills & Experience

• Modern test frameworks (e.g., pytest, JUnit, NUnit, Playwright, Cypress) and API testing (REST, gRPC)
• Containerization and environments: Docker; Kubernetes a plus
• Relational databases and SQL; ability to validate data pipelines and analytics outputs
• Experience with Elasticsearch or similar text‑retrieval data stores
• Performance/load testing (e.g., JMeter, k6, Locust) and profiling/observability
• Cloud experience (AWS/Azure/GCP) and Infrastructure‑as‑Code (e.g., Terraform)
• Experience building AI‑enabled automation workflows (e.g., Claude/OpenAI APIs, prompt patterns for test generation, repo‑aware RAG, scripts/services that turn AI output into runnable tests)
• Familiarity with agent frameworks (LangChain, LlamaIndex, AutoGen, or equivalents) and their tradeoffs in production quality pipelines
• Experience designing evaluation harnesses for non‑deterministic AI systems — statistical confidence, behavioral consistency, and regression detection
• Agile software development experience (Scrum / XP)

Why Join Us?

At NiCE, we don’t just connect systems—we connect people, platforms, and possibilities. In this role, you’ll be at the heart of driving product unification, governance, and go‑to‑market alignment across a mission‑critical platform. You’ll join a team that breaks down silos and enables seamless customer experiences across our product ecosystem.

What’s in it for you?

Join an ever‑growing, market‑disrupting, global company where the teams – comprised of the best of the best – work in a fast‑paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr!

Enjoy NiCE‑FLEX!

At NiCE, we work according to the NiCE‑FLEX hybrid model, which enables maximum flexibility: 2 days working from the office and 3 days of remote work, each week. Naturally, office days focus on face‑to‑face meetings, where teamwork and collaborative thinking generate innovation, new ideas, and a vibrant, interactive atmosphere. This role is located in our UT office located at 75 West Towne Ridge Parkway, Sandy, Utah 84070. https://www.nice.com/company/global-locations

NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.