Senior Associate - Quality Engineer, AI & Automation

New York Life•New York, NY

15h•$81,000 - $115,500•Hybrid

About The Position

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.

Requirements

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 TypeScript.
Experience building automated tests using tools and frameworks such as pytest, Selenium, mabl, Playwright, Cypress, REST Assured, Postman/Newman, Cucumber/BDD, JUnit, TestNG, or equivalent.
Strong API testing experience, including REST services, schema validation, contract testing, negative testing, authentication, authorization, and integration flows.
Working knowledge of SQL and data validation, including reconciliation-style testing across systems, files, APIs, databases, and reports.
Experience integrating automated tests into CI/CD pipelines using tools such as Jenkins, GitHub Actions, GitLab CI, Azure DevOps, or equivalent.
Familiarity with Git, pull requests, branching strategies, code reviews, and software engineering SDLC practices.
Ability to review developer unit test strategies and identify missing scenarios, weak assertions, poor mocks, inadequate boundary testing, and fragile coverage.
Understanding of quality patterns for distributed systems, including observability, logging, monitoring, resilience, retries, idempotency, data contracts, and environment stability.
Experience using AI-enabled developer tools, test generation tools, or LLM-based productivity tools to improve engineering delivery.
Strong analytical skills and the ability to turn defect trends, test failures, and business risk into practical engineering action.
Clear communication skills with the ability to explain technical quality risks to engineers, product owners, and business partners.
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.

Nice To Haves

ISTQB Foundation, ISTQB Advanced Test Automation Engineer, or equivalent practical experience.
Experience with wealth management workflows such as client onboarding, account opening, advisor desktop tools, portfolio management, holdings, balances, transactions, managed accounts, performance reporting, financial planning, or custodial integrations.
Experience with cloud platforms, containers, service virtualization, test data automation, or ephemeral test environments.
Experience with contract testing tools such as Pact or OpenAPI-based validation.
Familiarity with observability tools such as Splunk, Datadog, Dynatrace, Grafana, OpenTelemetry, or similar.
Experience testing AI-enabled applications, including model output validation, guardrail testing, regression evaluation, and auditability.
Exposure to regulated financial services controls, including data privacy, auditability, access control, and release governance.

Responsibilities

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.