AI Quality Architect

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. So, what’s the role all about? As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline — from requirement ingestion to autonomous deployment — is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up. How will you make an impact? Agentic Quality Architecture Design the end-to-end quality architecture for agentic SDLC pipelines — spanning requirement analysis, code generation, test creation, execution, triage, and release gates Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines Establish trust and verification models for agent-produced artifacts — test code, assertions, coverage reports, and defect analyses must all be auditable and traceable Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation AI-Native Test Engineering Platform Design and own the LLM-powered test generation platform — from natural language requirement ingestion to executable, maintainable test output Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time Build the self-healing test infrastructure layer — agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact Quality Gates in Autonomous Pipelines Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD — gates that reason about risk, not just pass/fail thresholds Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org 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 agent task completion accuracy, hallucination rates, context retention, and decision quality over time Architect drift detection systems that identify when agent behavior changes between model versions, prompt updates, or context window shifts Define adversarial and edge-case testing methodologies for AI features: prompt injection resistance, boundary condition handling, and graceful degradation under ... (truncated, view full listing at source)