Senior Software Engineer
Blink Healthabout 3 hours ago
Responsibilities
- Design and implement end-to-end automation frameworks and testing strategies for complex, cross-team features.
- Own the automation roadmap for one or more modules, defining goals, execution plans, and success metrics.
- Anticipate risks, identify testability gaps, and drive continuous improvements in quality and efficiency.
- Build and maintain functional, performance, resiliency, and security automation tools integrated into CI/CD pipelines.
- Design scalable test infrastructure for distributed systems, event-driven architectures, and cloud-native platforms.
- Collaborate across engineering, product, platform, and QA teams to ensure comprehensive coverage and reliable releases.
- Leverage AI-assisted automation tooling to accelerate test creation, maintenance, debugging, root-cause analysis, and release validation.
- Build AI-first automation workflows using LLM-powered tooling, intelligent test generation, self-healing automation, and agentic QA approaches.
- Drive innovation in autonomous quality engineering systems, including AI-assisted CI/CD validation, failure triaging, and predictive quality analysis.
- Champion modern quality engineering practices including observability-driven testing, production validation, synthetic monitoring, and intelligent automation standards.
- Provide technical mentorship to SDETs and engineers, elevating automation design, AI-assisted engineering practices, and code quality across teams.
Requirements
- At least 5+ years of experience in SDET contributions, automation engineering, and quality tooling.
- Passionate about delivering high-quality, reliable products through automation and intelligent testing strategies for both frontend and backend systems.
- Advanced proficiency in testing methodologies, automation frameworks, debugging techniques, and distributed system validation.
- Experienced in building and maintaining test infrastructure across multiple platforms and services.
- Strong command over algorithms, data structures, databases, and scripting languages (SQL, Python, JavaScript, TypeScript, or Java).
- Hands-on experience with CI/CD pipelines, performance engineering tools, observability platforms, and monitoring systems.
- Deep expertise in API and backend testing, ideally with experience in AWS and distributed systems such as Kafka, Kinesis, or event-driven architectures.
- Hands-on experience setting up performance/load/stress testing infrastructure at scale.
- Practical experience using AI-assisted development and automation tools to improve test coverage, productivity, and release confidence.
- Understanding of modern AI-first QA concepts including intelligent test generation, self-healing automation, autonomous validation workflows, and agentic QA systems.
- Comfortable evaluating and integrating emerging AI tooling into engineering and quality workflows.
- Strong systems thinking and can identify opportunities where AI can improve reliability, observability, and engineering efficiency.