AI Architect for Test Automation

Required Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Computer Engineering or a related discipline.
• 10+ years of relevant software engineering, AI/ML, automation or test engineering experience, with significant experience in technical leadership or architecture.
• Strong hands-on experience developing AI/ML solutions rather than only consuming AI tools.
• Strong experience with Python and at least one additional programming language such as C++, Java, C#, JavaScript or TypeScript.
• Strong experience designing and implementing test automation frameworks.
• Strong understanding of software testing methodologies, automation architecture and CI/CD.
• Demonstrated experience applying Machine Learning, Generative AI, LLMs or AI agents to real-world engineering problems.
• Experience designing AI/ML pipelines, model evaluation and deployment.
• Strong understanding of APIs, software architecture, distributed systems and integration.
• Experience working with Git, CI/CD and modern DevOps practices.
• Strong analytical and problem-solving capabilities.

Strongly Preferred

• Experience building AI-powered or autonomous test automation solutions.
• Experience with LLMs, RAG, prompt engineering, agentic AI and AI orchestration frameworks.
• Experience with frameworks/tools such as PyTorch, TensorFlow, scikit-learn, LangChain, LangGraph, LlamaIndex or equivalent.
• Experience with Selenium, Playwright, Appium, Robot Framework, pytest or equivalent automation technologies.
• Experience integrating AI/ML solutions with existing test automation frameworks.
• Experience working with embedded systems, IoT, wireless communications, telecommunications, consumer electronics, automotive or other hardware/software products.
• Knowledge of RF, wireless protocols, LTE, DMR, TETRA or radio communication systems would be a significant advantage.
• Experience analysing large volumes of test logs, telemetry or system data using AI/ML.
• Experience with cloud platforms such as AWS, Azure or GCP is an advantage.

Experience with Docker/Kubernetes is an advantage.