Automation Test Architect - Agentic AI

Wolters Kluwer•Coppell, TX

3d•Onsite

About The Position

We are seeking a Test Automation Architect to define and lead an enterprise-grade test automation strategy across UI, API, data, and platform layers—while evolving our quality engineering capabilities to support Agentic AI / LLM-based systems (probabilistic outputs, confidence-based validation, hallucination detection, and model evaluation). This role will architect scalable automation frameworks, drive engineering excellence, and accelerate delivery through highly productive, maintainable automation programs.

Requirements

8–12+ years in Quality Engineering / Test Automation with architect-level ownership of frameworks and strategy.
Proven experience designing scalable automation across: API testing (contract + integration), UI automation with stable locator strategies (test IDs, accessibility roles), Test data strategy and environment reliability.
Strong programming skills in at least one modern language (e.g., Java, Python, TypeScript/JavaScript, C#).
Expertise with automation tools/frameworks (examples): UI: Playwright / Cypress / Selenium, API: REST-assured / SuperTest / Postman/Newman / pytest, CI: GitHub Actions / Jenkins / Azure DevOps.
Demonstrated ability to reduce flakiness and improve execution speed through engineering solutions.

Nice To Haves

Experience testing LLM/Agentic AI features or other probabilistic systems, including: evaluation harnesses, benchmark datasets, semantic scoring, and drift detection, prompt/version regression strategies and structured evaluation reports.
Familiarity with modern quality approaches: test pyramid, contract testing, consumer-driven contracts, risk-based testing, shift-left.
Knowledge of security/performance testing integration (baseline understanding; hands-on is a plus).
Experience building reusable automation libraries for multiple teams/products.

Responsibilities

Define and own the test automation architecture and multi-layer strategy (UI/API/contract/integration/e2e), aligned to product risk and release cadence.
Establish automation standards, patterns, and reference implementations (framework structure, page-object/screenplay usage, test data patterns, reporting).
Drive shift-left testing practices (contract testing, component testing, pre-merge validation) and reduce reliance on fragile end-to-end tests.
Design and implement testing approaches for non-deterministic AI outputs, including: confidence/threshold-based assertions, semantic similarity checks and rubric-based validation, goldens/benchmark datasets and evaluation harnesses, hallucination and groundedness testing strategies, regression detection across model/prompt/version changes.
Define AI test coverage across accuracy, robustness, bias/safety (as applicable), performance, and reliability.
Partner with ML/AI, Product, and Engineering to establish acceptance criteria for AI capabilities and measurable quality gates.
Build and scale high-throughput automation projects that are reliable, fast, and easy to extend.
Improve test execution time, reduce flaky tests, and optimize CI/CD pipelines (parallelization, test selection, retries with guardrails).
Create actionable, engineering-friendly quality dashboards (pass/fail trends, flake rate, coverage, defect escape metrics).
Integrate automation into CI/CD with quality gates (PR checks, nightly suites, release readiness criteria).
Standardize observability for testing: logs, tracing hooks, screenshots/video, artifacts, and test run diagnostics.
Define practices for test environments, data management, service virtualization, and dependency control.
Serve as the QE technical authority: coach engineers on automation design, code quality, and best practices.
Lead design reviews for test architecture and automation PRs; enforce coding standards and maintainability.
Influence roadmap and delivery by translating risk into clear testing priorities.