Lead Software Test Automation Engineer - Test Architect - Agentic AI

Responsibilities

• Automation Strategy & Architecture
• 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.
• Agentic AI / LLM Test Model Alignment
• 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.
• High-Productivity Automation Programs
• 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).
• Tooling, CI/CD, and Quality Gates
• 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.
• Technical Leadership & Mentorship
• 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.
• Required Qualifications
• 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 .
• Preferred Qualifications (Strongly Desired)
• 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.
• Our Interview Practices
• Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.

Benefits

Additional Information

We are seeking a Lead 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.

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