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, house holding, 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, Dev Sec Ops , 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.

* Improved visibility into quality health, release risk, and automation value.

* Business partners trust the quality signals because the automation reflects real advisor and client workflows.

What You'll Bring

* 3+ years of hands-on experience in quality engineering, software engineering, SDET, or test automation roles.

* Direct experience in wealth management, brokerage, advisory, asset management, insurance/annuity platforms, or closely related financial services technology.

* Strong coding ability in at least one modern language, preferably Python, Java, JavaScript, or Type Script.

* Experience building…