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About This Role
Job Description
-------------------
#### Requisition ID
94418
#### 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
We are building a small, senior AI engineering team responsible for creating New York Life's enterprise AI platform—and the first generation of agentic AI solutions that run on it.
This team sits at the intersection of AI platform engineering, agentic system design, cloud architecture, and enterprise governance. Our mission is to build secure, governable, production\-ready AI capabilities on top of enterprise data, APIs, and cloud\-native platform services, moving beyond pilots to scalable business outcomes.
As AI Platform Lead, you will define how agentic AI is architected, deployed, secured, evaluated, and scaled responsibly across the enterprise. You will lead the team building the company's AI backbone—the governed platform that powers enterprise AI applications and enables the first wave of agentic insurance solutions. This is a rare opportunity to shape a greenfield platform with executive sponsorship, modern cloud technologies, and a small, highly experienced engineering team.
This is a hands\-on player\-coach role for an engineering leader who enjoys deep technical ownership across the full technology stack—from cloud infrastructure and runtime architecture to tool orchestration, retrieval, memory, evaluation, and agent behavior. You will establish the technical vision, mentor senior engineers, and remain actively involved in architecture, coding, and solution delivery.
The platform is built on Google Cloud and designed to leverage cloud\-native capabilities while maintaining portability through open architectures and reusable engineering patterns. Success in this role requires balancing innovation with governance, enabling responsible AI adoption in a highly regulated enterprise while delivering measurable business value.
Our Engineering Principles
Our team is intentionally small, senior, and highly technical. Regardless of title, every engineer is expected to build AI systems—and build with AI.
- Build agents that power the platform. Develop platform capabilities as intelligent agents—not just traditional services. Examples include lifecycle management agents that register, version, monitor, govern, and retire AI assets across the enterprise.
- Build cloud agents that plan and implement. Create agents that can translate business needs into implementation plans, orchestrate the required skills and tooling, and execute work with human oversight at the appropriate checkpoints.
- Build end\-to\-end multi\-agent solutions. Design and implement solutions where specialized agents collaborate to architect systems, provision infrastructure, generate code, validate through dedicated testing agents, deploy applications, perform post\-deployment verification, and maintain complete operational traceability.
- Build with AI\-assisted engineering tools. Be fluent with modern AI development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or equivalent technologies. AI\-assisted software development is a core engineering competency and will be evaluated throughout the interview process.
What You'll Do:
- Define the technical vision and reference architecture across the enterprise AI platform, including agent lifecycle services, orchestration capabilities, retrieval and memory services, and AI control plane components that integrate with enterprise AI services.
- Own and execute the multi\-phase platform roadmap, delivering foundational AI platform capabilities, reusable platform services, enterprise knowledge capabilities, and end\-to\-end builder agents that accelerate AI solution delivery.
- Lead, mentor, and develop a high\-performing team of senior AI engineers while maintaining hands\-on ownership of architecture, engineering, and agent development. Establish a high engineering bar and champion AI\-assisted software development practices.
- Partner with Security, Risk, Legal, and enterprise stakeholders to operationalize AI governance, automate solution review processes, and ensure alignment with enterprise AI risk management standards and responsible AI practices.
- Drive cloud architecture decisions, build\-versus\-buy evaluations, and platform portability strategies while leveraging Google Cloud capabilities without creating unnecessary vendor lock\-in.
- Establish AI FinOps capabilities, including model usage visibility, infrastructure cost attribution, dashboards, and budget guardrails that enable responsible scaling across the enterprise.
- Partner with business and technology leaders to deliver the first generation of enterprise agentic AI solutions that demonstrate measurable business outcomes.
- Build agents that improve the platform itself while leveraging AI\-assisted engineering throughout the software development lifecycle.
What You'll Bring:
Required Skills
- Proven experience leading the delivery of enterprise AI platforms, ML platforms, developer platforms, or cloud\-native engineering platforms operating at production scale.
- Deep expertise with cloud architecture and modern infrastructure, including Google Cloud Platform (preferred), Kubernetes, containers, Infrastructure as Code (Terraform), CI/CD, networking, identity, and observability.
- Strong experience designing and building production\-grade generative AI and agentic systems, including LLMs, retrieval\-augmented generation (RAG), memory architectures, multi\-agent orchestration, tool integration, Model Context Protocol (MCP), agent\-to\-agent (A2A) communication, and evaluation frameworks.
- Experience implementing AI governance, security, and responsible AI practices within regulated industries, including AI risk management, runtime guardrails, model controls, privacy, and compliance considerations.
- Demonstrated ability to lead senior engineering teams, establish technical direction, influence executive stakeholders, and successfully deliver complex technical roadmaps.
- Strong software engineering background with production experience in Python and modern software architecture.
- Experience using AI\-assisted software development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or similar technologies to accelerate engineering productivity.
- Excellent communication and collaboration skills with the ability to influence both technical and business stakeholders.
Preferred Skills
- Experience building enterprise AI platforms within financial services, insurance, or another highly regulated industry.
- Experience with Vertex AI, LangChain, Google ADK, AutoGen, CrewAI, OpenTelemetry, and modern AI platform technologies.
- Knowledge of enterprise AI governance frameworks including NIST AI RMF, ISO 42001, SOC 2, HIPAA, GDPR, or emerging AI regulations.
- Experience leading greenfield platform initiatives and building high\-performing engineering organizations.
Why This Role
- Build the foundation. Define the architecture, standards, and operating model for enterprise AI across New York Life.
- Lead a senior team. Join a small, highly experienced engineering organization where technical excellence, ownership, and collaboration are the norm.
- Work on frontier technology. Build production\-scale agentic AI systems and AI platforms that solve meaningful business problems in a highly regulated environment.
- Create enterprise impact. Shape how AI is responsibly adopted across the company and deliver capabilities that will enable teams across the enterprise for years to come.
- Stay hands\-on. Continue building, architecting, and shipping while leading a team of exceptional engineers.
Location
We are committed to attracting exceptional talent and will consider flexible work arrangements for the right candidate.
Pay Transparency
Salary Range: $147,500\-$211,000
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: 94418
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Salary Context
This $147K-$211K range is above the median 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. This role's midpoint ($179K) sits 17% below the category median. Disclosed range: $147K to $211K.
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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