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Job Description
Requisition ID
94871
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 Summary
As AI adoption expands rapidly across the enterprise, New York Life’s governance function must evolve significantly beyond the traditional review-and-approval model. The current team has established a strong foundation in AI model validation; the next phase requires a technology-enabled governance organization centered on automation, enablement, and continuous oversight.
The Head of AI Governance will build and lead that capability. The role pairs deep governance, risk, and regulatory expertise with hands-on command of modern AI technology — because the governance itself will increasingly be delivered through AI agents operating across the software development lifecycle. Equally important is the operating philosophy: AI Governance acts as a coach and enabler, not simply a judge. The objective is to help business and delivery teams adopt AI safely and quickly, rather than to say "no."
Key Responsibilities
AI Governance Enforcement Through Agentic Solutions
Lead the design, development, and operation of a suite of AI governance agents, including:
- AI model security and backdoor scanning agents operating at and after deployment.
- AI model risk classification and monitoring agents spanning the full SDLC through post-deployment.
- AI compliance agents operating continuously across planning, development, deployment, and production.
- AI legal agents that assess terms of use, contracts, licensing obligations, permitted use cases, and regulatory requirements.
- AI auditing agents spanning the full SDLC through post-deployment.
Ensure the governance agents integrate with existing enterprise systems and control frameworks, including audit, compliance, risk, and security platforms, so that oversight is continuous, evidenced, and auditable rather than point-in-time.
Expert Governance Review and Enablement Team
- Lead a highly experienced team responsible for reviewing complex scenarios that cannot be fully resolved by governance agents.
- Ensure the team does not function as a simple approval-or-rejection body. When a proposal cannot proceed as requested, the team identifies alternative approaches and practical paths forward.
- Establish service levels, escalation paths, and decision rights that keep governance responsive to the pace of delivery.
- Build governance literacy across the business and technology organizations through guidance, patterns, and reusable controls.
Internal and External Representation
- Represent New York Life AI in enterprise governance, risk, compliance, legal, and technology forums, including senior committees.
- Participate in external AI governance, legal, regulatory, and industry working groups to ensure New York Life remains aligned with evolving standards and best practices.
- Support regulatory examinations, audits, and due diligence requests relating to AI.
Governance Framework Evolution
- Continuously identify emerging governance needs and risks as the technology and regulatory landscape evolves.
- Define, update, and improve AI guardrails, standards, policies, and control frameworks.
- Ensuring governance remains practical, business-aligned, and responsive to the pace of AI innovation.
- Establish metrics and reporting that give senior leadership a clear, current view of AI risk posture across the enterprise.
Enterprise-Wide Governance Coverage
Lead governance across three distinct tiers:
- Tier 1 — Enterprise AI applications operating within New York Life-managed environments and tenants.
- Tier 2 — Employee productivity tools, personal AI assistants, and agentic workflows.
- Tier 3 — Vendor-provided AI and SaaS solutions where AI capabilities operate outside New York Life-managed environments.
Tier 3 is expected to require substantial focus. Vendors vary significantly in how they process, retain, govern, and utilize New York Life business data, and effective governance in this area will become increasingly critical as AI capabilities are embedded into more third-party platforms.
Qualifications
Required
- 12+ years of experience across technology risk, model risk management, AI/ML governance, compliance, or a closely related discipline, including significant leadership responsibility.
- Demonstrated experience building or substantially modernizing a governance function — not solely operating an existing one.
- Hands-on fluency with modern AI technology: foundation models, agentic systems, MLOps, evaluation and monitoring tooling, and AI security concerns such as prompt injection, data leakage, and supply-chain risk.
- Working command of the AI regulatory and standards landscape relevant to U.S. insurance and financial services, including NIST AI RMF, NAIC model guidance, NYDFS expectations, SR 11-7 model risk principles, and emerging state and international AI regulation.
- Proven ability to influence and partner with Legal, Compliance, Risk, Audit, Information Security, and Procurement at a senior level.
- Experience governing third-party and vendor-embedded AI capabilities.
- Executive communication skills, including the ability to present to board-level and regulatory audiences.
Preferred
- Insurance or financial services experience in a regulated, enterprise-scale environment.
- Prior engagement in external AI governance, standards, or industry working groups.
- Technical background sufficient to specify and oversee the build of governance automation and agents.
- Advanced degree in law, risk, computer science, statistics, or a related field.
Pay Transparency
Salary Range: $270,000 - $320,000
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: 94871
Salary Context
This $270K-$320K range is above the 75th percentile for AI Safety roles in our dataset (median: $187K across 7 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Safety positions make up 0% of the market. At New York Life, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills in Demand for This Role
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Safety roles pay a median of $253,100 based on 57 positions with disclosed compensation. This role's midpoint ($295K) sits 17% above the category median. Disclosed range: $270K to $320K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include Research Engineer ($252,000) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
New York Life AI Hiring
New York Life has 21 open AI roles right now. They're hiring across AI Safety, AI Software Engineer, AI/ML Engineer, Data Scientist. Positions span New York, NY, US, New York, NY, Tampa, FL. Compensation range: $92K - $320K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI Safety roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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