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About This Role
Job Description
-------------------
#### Requisition ID
94490
#### Department
Tech Data AI Ventures
#### Job Function
Tech Data AI Ventures
#### Location
New York,New York,United States
#### Role Location Designation
Hybrid \- 3 days per week
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.
- 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 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.
Preferred Qualifications
- 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.
Why This Role Matters
Quality in wealth management is not cosmetic. A missed field, stale holding, broken integration, or incorrect account balance can erode trust fast. This role helps New York Life deliver software with confidence by making quality part of the engineering fabric—not a checkpoint at the end of the road.
You will help move the organization to intelligent, automated, AI\-accelerated quality engineering. The work is hands\-on, technical, and business\-critical. The payoff is simple: faster delivery, fewer surprises, and platforms that advisors and clients can trust.
Pay Transparency
Salary Range: $81,000\-$115,500
Overtime eligible: Exempt
Discretionary bonus eligible: Yes
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Company Overview
At New York Life, our 180\-year legacy of purpose and integrity fuels our future. As we evolve into a more technology\-, data\-, and AI\-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities—inviting bold thinking, collaborative problem\-solving, and purpose\-driven innovation. Here, you’ll find the rare balance of long\-standing stability and forward momentum, supported by an inclusive team that honors tradition while embracing progress.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and deliver solutions that matter. Your ideas drive what’s next, and your growth powers it.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work.Click hereto discover more about our comprehensive benefit options or visit our NYL Benefits Site.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities. Click here to learn more about New York Life’s leadership in this space.
Recognized as one of *Fortune’s* World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners. To learn more about career opportunities at New York Life, please visit the Careers page of www.NewYorkLife.com.
Visit our LinkedIn to see how our employees and agents are leading the industry and impacting communities.
Visit our Newsroom to learn more about how our company is constantly evolving to meet our clients' and employees’ needs.
Job Requisition ID: 94490
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Salary Context
This $81K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At New York Life, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($98K) sits 54% below the category median. Disclosed range: $81K to $115K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
New York Life AI Hiring
New York Life has 15 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, Data Scientist, Data Engineer. Positions span New York, NY, US, White Plains, NY, US. Compensation range: $72K - $230K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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