Test Automation Engineer

Job Responsibilities:

• AI Platform Architecture & Implementation: Design, build, and deploy intelligent AI-driven platform capabilities that enable automation, intelligent insights, and advanced decision-making across distributed services and data systems.
• Product Innovation: Drive the development of next-generation AI-enabled platform features that enhance operational efficiency, enable intelligent automation, and deliver scalable, high-performance digital experiences.
• Generative AI Integration: Seamlessly integrate generative AI capabilities into platform services, including prompt engineering, Retrieval-Augmented Generation (RAG), intelligent workflows, and contextual reasoning systems.
• Intelligent System Leverage: Utilize Large Language Models (LLMs), embeddings, and vector-based retrieval systems to build intelligent, context-aware solutions that enhance platform capabilities and automation.
• AI Quality & Evaluation: Develop frameworks and methodologies for validating AI system performance, including response accuracy, model reliability, safety evaluation, and output consistency.
• AI Testing & Validation: Design and implement automated validation strategies to ensure reliability and correctness of AI-driven features, including automated test generation, model evaluation, and workflow validation.
• QA Automation Engineering: Develop and maintain scalable automation frameworks for validating platform services, APIs, and AI-enabled capabilities using modern testing tools and frameworks.
• API & Integration Testing: Build automated test suites to validate API functionality, service integrations, and distributed system interactions to ensure reliability and performance.
• Continuous Testing & CI/CD Integration: Integrate automated testing into CI/CD pipelines to enable continuous testing, faster feedback loops, and high-quality releases.
• Cross-Functional Collaboration: Work closely with engineering, product, data, and platform teams to translate complex requirements into scalable AI-enabled solutions while maintaining high-quality engineering standards.
• Technical Leadership: Provide guidance on AI engineering practices, automation strategies, and quality engineering standards to support platform reliability and intelligent system development.
• Mission Alignment: Contribute to building innovative, reliable, and ethically developed AI-powered systems that improve automation, developer productivity, and data-driven decision-making.

Qualifications:

• Experience: 8+ years of experience in software engineering, quality engineering, or platform engineering, including experience building automation frameworks and AI-enabled systems.
• Generative AI & LLM Integration: Hands-on experience integrating Large Language Models (LLMs) into applications, including prompt engineering, RAG architectures, AI workflow orchestration, and intelligent automation systems.
• Automation Engineering Expertise: Strong experience designing and implementing test automation frameworks using tools such as Playwright, Selenium, Cypress, TestNG, JUnit, or similar technologies.
• API Testing & Integration: Experience building automated tests for RESTful APIs and microservices using tools such as Postman, REST Assured, or similar frameworks.
• Programming Proficiency: Strong programming skills in at least two of the following languages: Python, Java, JavaScript, or C#.
• Cloud Platform Experience: Experience building and deploying applications on major cloud platforms such as AWS, GCP, or Azure, including containerized environments and CI/CD pipelines.
• Data & AI Technologies: Familiarity with vector databases, embeddings, AI model evaluation techniques, and data pipelines that support intelligent applications.
• System Reliability & Quality: Experience ensuring system reliability through automated testing, continuous integration, performance validation, and platform monitoring.
• DevOps & CI/CD: Experience integrating automated testing and validation frameworks within CI/CD pipelines for scalable platform delivery.
• Soft Skills: Strong communication and collaboration skills with the ability to work across engineering, product, and platform teams to deliver intelligent, production-ready solutions.

Behavioral Competencies:

• Cultivates Innovation
• Decision Quality
• Manages Complexity
• Drives Results
• Business Insight