Senior QA Engineer / AI-Driven Test Automation Lead, Counterparty Dashboard, Security Service, CPA/CDA, VP
Core
Lead the transformation of testing using AI across business-critical platforms including batch systems, microservices, and user interfaces to ensure quality is engineered into every layer of the stack.
Role type
Senior IC machine-learning engineer (audio)
Builds
Production ML models
Domain
Financial services (counterparty risk, security services, CPA/CDA)
Deliverable
production ML models
Required skills
Test automation strategy definition, AI/LLM-based test generation, self-healing automation, batch and data validation, CI/CD integration, Java, Python/TypeScript/Groovy, performance testing, observability tooling
Preferred skills
Experience with AI-powered testing platforms, prompt engineering for QA workflows, shift-left testing practices, mentoring QA engineers
Technologies
Selenium, Playwright, Cypress, RestAssured, Karate, Cucumber, TestNG, JUnit, Kafka, MQ, Jenkins, GitHub Actions, GitLab CI, Azure DevOps, JMeter, Gatling, k6, Splunk, ELK, Grafana, Dynatrace
Responsibilities
Define end-to-end test automation strategy spanning batch systems, microservices/APIs, and UI layers; Introduce and scale AI tools for test case generation, test data synthesis, and intelligent defect detection; Embed quality early in the SDLC by enabling developers and QA engineers to detect issues during design and coding stages; Establish standards, reusable frameworks, and best practices for automation across functional, regression, performance, and resilience testing; Drive high-quality, predictable releases through robust CI/CD-integrated test pipelines, quality gates, and automated regression suites; Use AI-assisted analytics to identify coverage gaps, prioritize tests based on risk, and optimize test execution time; Architect automated validation strategies for batch jobs, data pipelines, and reconciliations typical of counterparty, security service, and CPA/CDA workflows; Evaluate, select, and roll out modern testing tools (AI-powered and traditional) across the engineering organization; Mentor QA engineers and SDETs on AI-driven testing practices, automation design, and quality engineering principles; Partner with engineering leads, product owners, and business stakeholders to align quality goals with delivery timelines and risk posture; Define and track quality KPIs (defect leakage, automation coverage, MTTR, escape rate) and continuously improve testing maturity