We re looking for an AI Test Architect to define, implement, and scale the next generation of quality engineering powered by AI. You will own the test architecture strategy end-to-end combining LLM-driven automation , computer vision , and hardware-in-the-loop (HIL) systems to deliver robust, scalable, and privacy-first testing for mobile applications and hardware-integrated products .

This is a senior, hands-on architecture role: you ll set the strategy, establish standards, lead technical decision-making, and build reusable platforms and frameworks while mentoring teams across QA, Dev, and DevOps.

What You ll Do 1) AI Test Strategy Architecture

- Define and maintain the enterprise test architecture , roadmap, and standards spanning functional, non-functional, integration, mobile, and HIL layers.

- Drive a shift-left and automation-first culture; architect frameworks that are modular, resilient, and easy to evolve.

- Establish test design principles , risk-based testing approaches, and coverage models aligned with business goals and compliance requirements.

- Lead architecture reviews and decision forums; evaluate build vs. buy for AI tooling and frameworks.

2) AI-Driven Test Innovation (LLMs, RAG, CV/OCR)
- Architect and implement RAG-based test generation using local LLMs (e.g., Ollama, llama.cpp ) and frameworks like LangChain/LlamaIndex to reason over requirements, app states, and logs.

- Build AI agents that can: interpret acceptance criteria, propose and prioritize test scenarios, and auto-generate test cases/scripts.

- Develop computer-vision and OCR pipelines (OpenCV, Tesseract, or equivalent) for precise UI validation , visual diffing, and visual regression analysis.

- Design models for UI anomaly detection , flakiness prediction, and self-healing locators beyond traditional Appium-style selectors.

- Automate localization testing for text, layout,



and formatting across languages and screen sizes using AI.

3) Hardware-in-the-Loop (HIL) Mobile Systems
- Define and evolve a Python-based HIL framework for end-to-end validation of Mobile/Desktop/WebApp interacting with hardware.

- Architect communication interfaces ( USB, Bluetooth, Serial ) and test harnesses to control/observe device interactions reliably at scale.

- Incorporate AI-driven adapters that learn device behaviors, detect drift, and improve robustness of HIL scenarios over time.

- Partner with mobile teams (Android/iOS) to integrate Appium/Espresso/XCUITest/ FlaUI where appropriate and augment with AI components.

4) On-Prem Infrastructure MLOps for Test at Scale
- Design on-prem/private-cloud inference infrastructure for low-latency, high-throughput model execution with strong data privacy guarantees.

- Containerize models and agents ( Docker ) and integrate into CI/CD (Jenkins, GitLab CI) for parallel execution and test-on-commit workflows.

- Implement continuous learning loops to leverage test failures, telemetry, and labels to retrain and improve models.

- Establish model lifecycle practices (versioning, evaluation, rollback, governance) using tools like MLflow/ Langchain , self-hosted vector stores, and caching.

5) Automation Platforms Tooling
- Extend or replace traditional frameworks (e.g., Appium ) with AI-assisted components that enhance stability and coverage.

- Build Python-based toolchains for model training/inference and integrations into test workflows; standardize reusable libraries and templates .





- Define reference architectures for UI, API, performance, security, and resilience testing ; ensure seamless observability, logging, and triage workflows.

6) Quality Governance, Metrics Risk
- Define quality gates , entry/exit criteria , and release readiness bars; ensure compliance and privacy-by-design.

- Track and publish leading indicators and outcome metrics defect escape rate, test yield, visual regression detection rate, flakiness, mean-time-to-detect/triage, inference latency, infra utilization.

- Conduct root cause analyses and drive systemic fixes across tooling, test design, and pipelines.

- Audit processes for adherence to standards; champion continuous improvement.

7) Technical Leadership Enablement
- Mentor QA engineers and SDETs on AI/ML testing, HIL, and architectural best practices.

- Facilitate collaboration across QA, Development, Data/ML, Security, and DevOps.

- Curate internal playbooks, patterns, sample repos, and training content to scale adoption.

What You Bring Must-Have
- 10+ years in Quality Engineering / Software Development, with 5+ years in test architecture / QA leadership .

- Solid Python expertise and hands-on experience with AI/ML in testing contexts.

- Proven experience training or fine-tuning models using PyTorch , TensorFlow , or Hugging Face .

- Solid background in Computer Vision (OpenCV) and OCR for UI/visual validation.

- Track record building automation frameworks for mobile and/or device-integrated systems , and clear understanding of limitations of traditional tools (e.g., Appium).

- Experience with CI/CD , Docker .

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Test Architect (Hyderabad)
🏢 WSAudiology
📍 Hyderabad