Senior QA Automation Engineer - Semiconductor

The Senior QA Automation Engineer will play a critical role in ensuring the accuracy, reliability, security, and performance of a modern AI-enabled integration platform.

This role extends beyond traditional UI testing. You will design and implement automation frameworks for backend services, cloud-native deployments, and machine learning-driven applications. You will be deeply involved in validating data pipelines, testing ML forecasts against historical datasets, and ensuring platform readiness across multi-cloud and containerized environments.

The ideal candidate brings a strong automation-first mindset, hands-on experience with ML application testing, and the ability to collaborate closely with engineering, data science, and Dev Ops teams to embed quality throughout the software development lifecycle.

• Design, develop, and maintain scalable automation frameworks using Python, Java, Selenium, and API testing tools.
• Implement automated testing for backend services, data pipelines, and cloud-native microservices.
• Build reusable test components for regression, integration, performance, and end-to-end testing.
• Continuously improve test coverage, execution efficiency, and automation reliability.

• Validate machine learning models by evaluating prediction accuracy against historical and real-world datasets.
• Design test strategies for forecasting, anomaly detection, and analytics pipelines.
• Verify data quality, model outputs, and system behavior across AI-driven workflows.
• Collaborate with data science teams to establish validation methodologies and quality metrics.

• Test applications deployed as Docker containers across multiple cloud environments.
• Contribute to Helm charts, deployment pipelines, and test environment management.
• Integrate automated testing into CI/CD pipelines and Dev Ops workflows.
• Support quality assurance activities across cloud-native and Kubernetes-based environments.

• Perform security testing using tools such as SQLMap, OWASP ZAP, and vulnerability scanning solutions.
• Validate system reliability, fault tolerance, disaster recovery, and recovery scenarios.
• Support observability testing using logs, metrics, traces, and monitoring platforms such as Prometheus and Grafana.
• Ensure applications meet security, performance, and operational readiness standards.

• Champion quality-driven engineering practices and shift-left testing strategies.
• Collaborate with product, engineering, Dev Ops, and data teams to define comprehensive test strategies.
• Mentor junior QA engineers and contribute to the continuous improvement of QA standards, processes, and automation practices.
• Drive adoption of best practices in software quality, automation, and testing methodologies.

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 6–10+ years of experience in QA Automation and software test engineering.
• Strong hands-on experience with:
• Automation frameworks including Selenium, Python, and Java
• API and backend testing
• Test design for distributed and cloud-native systems
• Experience testing AI/ML-based applications, including data validation and model accuracy verification.
• Solid understanding of cloud-native architectures, Docker, and Kubernetes.
• Experience integrating automated testing into CI/CD pipelines.
• Strong analytical, troubleshooting, and problem-solving skills.
• Experience working within Agile/Scrum development environments.

• Client-facing experience supporting customer engagements, pre-sales activities, or business development initiatives.
• Exposure to data engineering pipelines and large-scale analytics platforms.
• Experience with Dev Ops practices, Infrastructure as Code (IaC), and platform automation.
• Knowledge of observability platforms such as InfluxDB, Prometheus, and Grafana.
• Experience working with SaaS or enterprise-scale software platforms.
• Familiarity with manufacturing systems, MES platforms (e.g., SiView, Fab
  300), and equipment automation.
• Knowledge of ERP integration strategies and Enterprise Application Integration (EAI).
• Ability to travel as needed to support customer engagements and business initiatives.
• Relevant certifications in cloud technologies, ITIL, ISO 20000, COBIT, or related disciplines.

• Help shape the quality strategy for a modern AI-enabled enterprise platform.
• Work with cutting-edge technologies across automation, cloud-native architectures, AI/ML, and large-scale data systems.
• Influence engineering quality standards, automation frameworks, and testing best practices.
• Collaborate with cross-functional teams solving complex enterprise and manufacturing technology challenges.
• Contribute to high-impact digital transformation initiatives used in real-world production environments.
• Grow your technical leadership skills while mentoring engineers and driving continuous improvement across the organization.