Lead SDET - Distributed Systems - US Based AI Enterprise Platform

As AI capabilities accelerate, every customer-touching function within the enterprise is being redefined. The future lies in the seamless connection between people, systems, and dataenabling more intuitive, human-centric experiences for both customers and employees.

Job Description

Key Responsibilities:

• Own and drive the QA vision and roadmap for AI-driven and SaaS platforms, continuously improving automation depth, test coverage, and release confidence.
• Architect and deliver scalable test automation solutions across APIs, UI layers, and data validation, leveraging modern AI-assisted automation techniques.
• Collaborate closely with Data Science and Engineering teams to validate AI/ML pipelines, analytics workflows, and model integrations in production-grade environments.
• Lead performance, security, and regression testing initiatives to ensure platform reliability, resilience, and scalability at enterprise scale.
• Conduct root cause analysis and quality trend reviews to identify systemic gaps and propose targeted automation or process improvements.
• Maintain comprehensive QA documentation, including test strategies, release artifacts, and best practices using tools like Jira and Confluence, aligned with Agile/SAFe methodologies.
• Ensure comprehensive end-to-end test coverage across microservices, APIs, mobile, and web applications deployed in multi-tenant cloud environments.

Qualifications

• Bachelors degree in Computer Science, Engineering, or a related field.
• 4+ years of experience in Software Quality Assurance with a strong emphasis on test automation.
• Advanced proficiency in at least one programming language such as Java, Python, JavaScript, or TypeScript.
• Strong ability to analyze complex features, author high-quality test cases, and develop automation scripts with meaningful assertions and validations.
• Proven experience designing, building, and maintaining custom automation frameworks.
• Hands-on experience testing distributed systems for end-to-end scenarios, long-running stability, reliability, migration, and performance.
• Experience defining and executing AI testing strategies, including validation of foundation models, prompt engineering, fine-tuning, RAG pipelines, and Generative AI frameworks (e.g., LangChain, Guidance).
• Solid understanding of CI/CD pipelines and tools such as Jenkins, GitHub Actions, GitLab CI, or CircleCI.
• Demonstrated ability to reason about complex, distributed architectures and create effective test strategies for them.
• Strong analytical thinking, debugging skills, and problem-solving capabilities.

Preferred Skills

• Exposure to microservices-based architectures and associated testing approaches.
• Experience with performance and load testing tools such as JMeter, Gatling, or k6.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure); familiarity with at least one is preferred.
• Prior experience working in Agile or Scrum-based delivery models.
• Experience mentoring or leading QA engineers.
• Background in testing data-intensive or data-driven applications.

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