Senior Associate - Quality Engineer, AI & Automation

Location Designation: Hybrid - 3 days per week

Role Overview New York Life is seeking a Senior Associate, Quality Engineer to help build modern, automation-first quality practices across our Wealth Management technology platforms. This is a hands-on engineering role for someone who can code, understand the business, challenge designs, and use AI-enabled tooling to improve how quality is built into software from the first requirement through production release.

This is not a manual testing role . The right candidate will design and build automated test frameworks, review developer unit test strategies, improve CI/CD quality gates, analyze defect patterns, and partner with engineers and product owners to make systems more testable, observable, resilient, and business-ready.

You will work across advisor, client, account, portfolio, transaction, data, integration, and reporting workflows that support wealth management outcomes in a regulated financial services environment. The role requires enough business fluency to know where quality risk hides: in account data, householding, balances, holdings, transactions, suitability-sensitive workflows, integrations, reports, and downstream advisor/client experiences.

What You’ll Do

• Design, build, and maintain automated test suites across API, UI, integration, data, regression, and end-to-end workflows.
• Write clean, maintainable automation code using modern engineering practices, including reusable libraries, test utilities, fixtures, mocks, service virtualization, and test data management.
• Use AI and GenAI-enabled tools to accelerate test design, coverage analysis, defect triage, test data generation, regression optimization, and failure pattern detection.
• Partner with software engineers to review unit test strategy, code coverage, edge-case coverage, mocks/stubs, contract tests, and test results before code moves downstream.
• Participate in design and architecture reviews to improve testability, observability, reliability, determinism, data validation, resiliency, and operational supportability.
• Build automation into CI/CD pipelines so quality signals are fast, visible, repeatable, and actionable.
• Develop automated quality gates for pull requests, builds, deployments, APIs, data contracts, and release readiness.
• Analyze recurring defects and production incidents to identify systemic quality gaps and drive root-cause prevention.
• Create dashboards and reporting that show meaningful quality health: automation coverage, failure trends, flaky tests, escaped defects, regression duration, release confidence, and risk hotspots.
• Collaborate with Product, Engineering, Architecture, DevSecOps, Release Management, and business stakeholders to define test strategy for complex wealth management features.
• Translate business scenarios into automation coverage that reflects how advisors, clients, operations teams, and downstream systems actually use the platform.
• Help raise the engineering bar by mentoring peers on automation design, test strategy, AI-assisted quality practices, and quality-by-design thinking.

AI & Technical Expectations The ideal candidate should be comfortable using AI as an engineering accelerator—not as magic dust sprinkled on stale test cases.

Expected Hands-on Capabilities Include

• Applying GenAI tools responsibly to generate, refactor, review, and maintain automation code.
• Using AI to summarize failures, cluster defects, detect flaky tests, identify regression risk, and improve coverage.
• Understanding prompt design, evaluation, reproducibility, privacy constraints, and human review when using AI in a regulated environment.
• Building or integrating automation utilities that leverage LLMs, embeddings, or intelligent heuristics where appropriate.
• Validating AI-assisted outputs rather than blindly trusting them.
• Working with APIs, SQL/data validation, CI/CD pipelines, source control, test frameworks, and cloud or containerized environments.

What Success Looks Like

• Increased automated coverage across high-value wealth management workflows.
• Reduced reliance on manual regression testing.
• Faster feedback to developers through CI/CD-integrated quality gates.
• Better unit, API, integration, and end-to-end test strategies.
• Fewer escaped defects and less defect recurrence.
• Cleaner architecture decisions because testability and operability are considered earlier.
• Improve…