The Ring Blink Customer Service (RBCS) team's application complexity has grown significantly, now serving thousands of external users 24/7 across multiple integrated platforms. We're rapidly deploying AI across voice, chat, and self-service channels, but our AI capabilities ship faster than we can validate them manually, creating a critical gap in our quality assurance process. The current testing team is overwhelmed with manual testing efforts, causing delays in release cycles and increasing the risk of production issues. Key challenges include managing multiple parallel releases, ensuring comprehensive integration testing across four major systems, maintaining high availability requirements (99.9% uptime), and meeting strict security compliance standards. We are seeking a Software Development Engineer in Test to establish and own the automated quality layer using AccelQ and other Amazon internal test automation tools, enabling us to ship AI features with confidence instead of slowing down to manually test every model change. This role will implement robust automation frameworks, conduct thorough integration testing, and ensure quality across all platforms. Without this crucial hire, we risk increased production incidents, delayed AI feature releases, customer dissatisfaction, team burnout, and technical debt accumulation. This role will be instrumental in supporting our business objectives of maintaining high system reliability, ensuring secure transactions, and enabling faster time to delivery of new AI-powered features.
Key job responsibilities
Key job responsibilities
The successful Software Development Test Engineer will be obsessed with customer experience and quality improvement. In this role, you will:
Code Development & Delivery
Write secure, stable, testable, maintainable code that consistently meets high quality standards
Apply best practices in all aspects of software development and testing
Test Automation & Framework Development
Design, develop, and execute comprehensive test automation frameworks using AccelQ and Amazon internal test automation tools for web applications, APIs, and integrated systems
Build scalable test automation solutions for Salesforce Lightning components, Amazon Connect flows, AI services, and AWS service integrations
Use technology to validate and verify software, seeking input from team members on the best software test techniques to utilize
Develop custom testing tools and utilities that enable the team to efficiently validate complex AI-powered workflows
Create reusable test libraries and patterns that accelerate test development across the organization
Establish automated validation pipelines for AI model outputs, conversation flows, and intent recognition accuracy
Quality Transformation & Process Improvement
Dive deep into testing methodologies to transform manual quality processes into highly automated quality solutions, specifically targeting AI validation bottlenecks
Improve your team's automation of development, testing, and deployment processes using Amazon's internal testing ecosystem
Design, develop, and execute automation test plans and reporting on test execution
Coordinate test approaches, test cases, and test methodology with remote teams
Drive continuous improvement in test coverage from current 65% to target 90%, with focus on AI-critical paths
Transform manual AI testing processes into highly automated, reliable quality gates
Integration & End-to-End Testing
Own the end-to-end testing strategy across Salesforce, Amazon Connect, AI services, and AWS platforms
Design and execute integration test suites that validate data flow and business logic across system boundaries
Implement API testing frameworks for RESTful services and AWS service integrations
Validate AI model outputs and ensure quality of AI-powered customer service features
Automate the validation of modern user interfaces and cloud infrastructures
Build automated testing for conversational AI flows, including edge cases and error scenarios
Performance & Security Testing
Conduct performance testing and capacity planning for high-traffic customer service applications and AI services
Implement load testing strategies to validate system behavior under peak conditions, including AI service performance
Implement security testing practices and validate compliance with Amazon security standards
Establish performance benchmarks and regression testing for critical user journeys
Create performance testing frameworks for AI services under varying load conditions
Team Collaboration & Mentorship
Communicate technical concepts and processes using clear, simple language and visuals
Collaborate with development teams to shift testing left and implement test-driven development practices
Work in Agile/Scrum development methodologies with cross-functional teams
Collaborate with AI/ML teams to implement test-driven AI development practices
