Senior AI-Enabled Software Engineer in Test

eClinical Solutions is transforming clinical development with elluminate®, their Clinical Data Cloud, helping life sciences organizations unify, analyze, and unlock the value of their data. As a Senior AI-Enabled Software Engineer in Test, you will lead the evolution of test automation strategies to enhance the quality and reliability of their flagship SaaS Clinical Data Intelligence Platform.

Responsibilities:

• Design, build, and maintain scalable automated test frameworks covering UI, API, integration, and performance layers across AWS-based .NET services
• Expand and refactor the existing automated test suite to increase coverage, reliability, and execution speed
• Leverage AI and agentic AI tools to generate, enhance, and maintain automated tests, systematically reducing manual regression testing
• Integrate automated tests into CI/CD pipelines to enforce quality gates and support rapid, high-confidence releases
• Analyze test results, investigate failures, and partner with engineers to diagnose and resolve defects across distributed systems
• Collaborate closely with developers and DevOps engineers to ensure new features are designed with testability, observability, and automation in mind
• Continuously improve test data management, environment configuration, and reproducibility of test runs
• Identify automation gaps and propose improvements that increase release cadence and reduce risk
• Actively participate in architectural discussions, sprint planning, backlog grooming, and milestone reviews; partner with Engineering Leads to define scope, technical approach, deliverables, and quality criteria for upcoming releases
• Contribute to documentation, standards, and best practices that elevate quality engineering across the organization
• Ensure audit readiness by maintaining complete, traceable documentation in alignment with SOPs and Quality Management System (QMS)
• Collaborate with DevOps and Cloud teams to optimize infrastructure scalability, reliability, and performance within compliance constraints
• Support defect triage, root cause analysis, CAPA processes, and continuous improvement initiatives
• Other duties as assigned

Requirements:

• Bachelor's Degree in Computer Science, Engineering, Data Science, or related discipline, or equivalent work experience preferred
• 10+ years of hands-on experience in enterprise software engineering and test automation (SDET), including at least 6 years designing and scaling automated test frameworks preferred
• Proven experience driving automation strategy and expanding adoption of automated testing across engineering teams
• Strong experience implementing automation across all layers: database validation (SQL), API/integration (REST, HTTP), and end-to-end UI testing
• Hands-on expertise with C#/.NET platform, Selenium and Playwright, TypeScript/JavaScript, HTML, and CSS
• Experience with Implementing AI-enabled monitoring and anomaly detection to proactively identify system degradation or performance drift
• Deep understanding of distributed systems, microservices architecture, and service-to-service communication
• Experience integrating automated tests into CI/CD pipelines and implementing quality gates
• Hands-on experience with containerization (Docker) and Kubernetes-based test execution environments
• Ensure audit readiness by maintaining complete, traceable documentation in alignment with SOPs and Quality Management System (QMS)
• Experience designing and executing performance/load testing strategies
• Strong collaboration skills; experience partnering with Development Leads and Product teams in planning, grooming, and milestone definition
• Demonstrated ability to influence engineering standards, mentor SDETs, and drive quality engineering best practices
• Experience with generating and maintaining performance validation documentation including test plans, test cases, traceability matrices, summary reports, and deviation documentation
• Programming/Scripting: Python, SQL, PowerShell, .Net
• Databases: SQL Server and large-scale data platforms
• Cloud Platforms: AWS
• Containerization & Orchestration: Docker, Kubernetes (preferred)
• Observability & Monitoring: Application Insights, Datadog, NewRelic
• Experience with CI/CD tools in validated environments (TeamCity, Octopus Deploy, Git-based workflows)