AI Test Architect

Role Overview

WS Audiology is seeking a senior, hands-on AI Test Architect to define and scale next-generation quality engineering. The role combines AI-driven test generation, computer vision, OCR, mobile automation and hardware-in-the-loop systems to deliver robust, scalable and privacy-first testing for mobile applications and hardware-integrated products. You will own test architecture strategy, establish standards, build reusable platforms and mentor QA, Development and DevOps teams.

Key Responsibilities

• Define enterprise test architecture, roadmap and standards across functional, non-functional, integration, mobile and HIL testing.
• Promote shift-left and automation-first practices through modular, resilient and maintainable frameworks.
• Establish risk-based test design, coverage models, quality standards and architecture review processes.
• Build RAG-based test generation using local LLMs such as Ollama or llama.cpp with LangChain or LlamaIndex.
• Develop AI agents that interpret acceptance criteria, prioritize test scenarios and generate test cases and scripts.
• Build computer vision and OCR pipelines using OpenCV, Tesseract or equivalent tools for UI validation and visual regression.
• Design approaches for UI anomaly detection, flaky-test prediction and self-healing locators.
• Automate localization testing across languages, screen sizes, layouts and formatting requirements.
• Define Python-based HIL frameworks for mobile, desktop and web applications interacting with devices, sensors and wearables.
• Build reliable device communication and test harnesses for USB, Bluetooth, serial interfaces and vendor SDKs.
• Integrate mobile automation tools such as Appium, Espresso, XCUITest and FlaUI where appropriate.
• Design private-cloud or on-premise AI inference infrastructure with strong data privacy and low-latency execution.
• Containerize AI models and agents with Docker and integrate testing into CI/CD pipelines such as Jenkins, GitLab CI and Azure DevOps.
• Establish model lifecycle processes covering versioning, evaluation, rollback, governance and continuous improvement.
• Standardize reusable Python libraries, reference architectures, logging, observability and defect-triage workflows.
• Define quality gates, release-readiness criteria and metrics covering defect escape rate, test yield, flakiness, triage time and inference latency.
• Lead root-cause analysis, drive systemic quality improvements and mentor QA engineers and SDETs.

Must-Have Qualifications

• 10+ years in Quality Engineering or Software Development, including 5+ years in test architecture or QA leadership.
• Strong Python skills and hands-on experience applying AI/ML to testing.
• Experience training or fine-tuning models using PyTorch, TensorFlow or Hugging Face.
• Solid computer vision and OCR knowledge, particularly OpenCV and UI/visual validation.
• Experience building automation frameworks for mobile or device-integrated systems, with an understanding of traditional tool limitations.
• Experience with CI/CD, Docker and deployment of AI models into test or production environments.
• Ability to define architecture strategy, establish standards and lead cross-functional technical initiatives.

Highly Desirable Skills

• Hands-on experience with local LLMs such as Ollama or llama.cpp and RAG frameworks such as LangChain or LlamaIndex.
• Hardware-in-the-loop or software-in-the-loop testing experience.
• Knowledge of Android and iOS tooling, including ADB, Xcode, Fastlane, Espresso, XCUITest or Detox.
• Experience with Prometheus, Grafana, OpenTelemetry and structured logging.
• Familiarity with medical-device or regulated environments and standards such as ISO 13485, IEC 62304, IEC 62366, ISO 14971, HIPAA or GDPR.

Leadership & Personal Competencies

• Strategic thinker with strong systems-design instincts and a builder's mindset.
• Excellent professional written and spoken English communication skills.
• Strong ownership, initiative and comfort navigating ambiguity.
• Ability to coordinate across QA, Development, Data/ML, Security and DevOps teams.
• Structured, analytical and detail-oriented approach to quality and compliance.
• Passion for mentoring engineers and improving engineering practices across global teams.

Success Metrics

• Target more than 40% reduction in flaky-test failures and more than 30% faster triage through AI-assisted analysis and visual diffing.
• Target more than 25% improvement in critical defect detection before release against the prior baseline.
• Maintain P95 AI inference latency within the defined CI pipeline service-level target.
• Increase adoption of standardized AI-assisted frameworks across product teams.
• Maintain documented compliance with privacy and model-governance standards.

Technology Stack

About WS Audiology

WS Audiology provides innovative hearing aids and hearing health services. With approximately 12,000 colleagues across 130 countries, the company aims to help more people access hearing care and improve their quality of life. WS Audiology is committed to an inclusive employee experience and equal opportunity for all.