VP, Head of New Frontier Technology

New York Life•Sanger, CA

11d•$275,000 - $325,000•Hybrid

About The Position

AI capability is advancing faster than any single enterprise can track from one location, and the organizations setting the pace — the frontier AI research labs and the hyperscale cloud and AI platform providers — are concentrated on the West Coast. New York Life needs a senior leader embedded in that ecosystem, accountable both for the relationships and for what the firm builds as a result. This is a leadership role in two distinct senses. It is a coverage role: the Vice President is New York Life's designated senior point of contact for frontier AI labs and hyperscaler platform providers, owning the technical dialogue, the early visibility that comes with those relationships, and senior AI leadership coverage in the Pacific time zone. It is also a people and delivery leadership role: the Vice President builds and leads a team that delivers in-house AI solutions — and the business cases behind them — into production. The scope of the role is the New York Life AI Center of Excellence (AI CoE), across three dimensions: process, platform, and people. The Vice President owns how AI work moves to production, what the AI CoE builds on and how governance is integrated into it, and the recruiting and upskilling that supply the capability to deliver. All three are accountable to the same outcome — production deliveries — and the mandate is explicitly not innovation for innovation's sake.

Requirements

12+ years of progressive experience in technology, including at least 5 years in senior leadership of AI, machine learning, or advanced engineering functions, with direct people leadership of engineering, platform, or architecture teams.
Established senior relationships across frontier AI labs, hyperscale cloud and AI platform providers, and the West Coast senior AI talent market.
Proven track record of building and leading teams that delivered AI solutions from concept to production at enterprise scale, with measurable business outcomes.
Demonstrated ownership of an engineering lifecycle for AI or machine learning systems — evaluation, testing, release, monitoring, and change management — at enterprise scale.
Architectural depth: demonstrated ownership of platform and reference architecture decisions for AI systems, including build-versus-buy judgment and integration into complex enterprise environments.
Experience integrating governance, risk, security, and compliance controls into a delivery platform or lifecycle in a regulated environment, as engineered capability rather than manual process.
Deep, current, hands-on understanding of the modern AI stack: foundation models, agentic architectures, retrieval, evaluation, orchestration frameworks, and AI infrastructure.
Demonstrated success attracting and recruiting senior technical talent, and a track record of building technical capability in others through enablement, training, or engineering academies.
Ability to build and defend a business case, and comfort being held accountable for value delivered rather than activity.
Executive presence and credibility sufficient to represent a Fortune 100 company to researchers, platform executives, and senior industry leaders.
Ability and willingness to operate across time zones and travel regularly to the New York Home Office.

Nice To Haves

Experience in financial services, insurance, or another highly regulated industry.
Prior experience at a frontier AI lab, hyperscaler, or large-scale AI platform organization.
Experience establishing or running a center of excellence, platform organization, or enterprise engineering standard.
Advanced degree in computer science, engineering, or a related quantitative discipline.
Public presence in the AI community — speaking, publishing, open-source contribution, or research.

Responsibilities

Serve as New York Life's designated senior relationship owner for frontier AI research labs and hyperscale cloud and AI platform and NYL solution providers — the primary impetus for establishing a West Coast presence.
Own the technical dialogue with these providers: model and product roadmaps, research directions, pre-release and early-access programs, and capacity and platform considerations, in partnership with Enterprise Architecture, Sourcing, and Information Security.
Maintain a defined coverage model — named providers, engagement cadence, briefing and roadmap review schedule, and a documented view of each provider's trajectory against the NYL AI CoE's needs.
Represent New York Life at technical conferences, research forums, and industry events where senior representation is expected, and act as a consistent point of contact for the external AI community.
Translate coverage into actionable intelligence for AI, technology, and business leadership: what is changing, what it means for the AI CoE roadmap and architecture, and what the firm should do about it.
Own the AI CoE's software development lifecycle: how an initiative moves from hypothesis and evaluation through prototype, pilot, production, and ongoing monitoring, with defined entry and exit criteria at each stage.
Establish the engineering practices particular to AI systems: evaluation-driven development, dataset and prompt versioning, regression and safety testing, red-teaming, human-in-the-loop review, and CI/CD adapted for models and agents.
Design governance, risk, security, privacy, and compliance checkpoints into the lifecycle so approvals are a planned step rather than a late-stage obstacle.
Own post-deployment discipline: observability, model and prompt change management, drift and quality monitoring, incident response, and deprecation and migration playbooks as providers retire and replace models.
Measure and improve cycle time from idea to production, and publish the lifecycle as the standard that delivery teams across New York Life can follow.
Own the identification, evaluation, and hands-on exploration of emerging technologies with direct relevance to the New York Life AI CoE — foundation models and model routing, agentic frameworks and orchestration, retrieval and context management, evaluation and observability, inference infrastructure, and new AI developing SDLC and tooling.
Maintain a prioritized evaluation portfolio tied to the AI CoE roadmap, with success criteria established up front; retire initiatives that do not clear them and scale those that do.
Own the architectural recommendation on adoption: reference architectures, integration patterns, build-versus-buy positions, and platform standards, developed with AI engineering teams and Enterprise Architecture.
Integrate enterprise governance into the platform itself — model inventory and registry, data access and entitlement patterns, logging and auditability, guardrails and content controls, and evaluation harnesses delivered as shared services — so that every solution built on the platform inherits its controls.
Keep the platform ahead of provider change, using coverage intelligence so that model and infrastructure shifts are anticipated rather than absorbed reactively.
Build and maintain a strong pipeline of senior AI talent sourced from frontier AI labs, hyperscalers, large-scale AI engineering organizations, and academic research groups, and engage senior technologists and researchers who could become New York Life AI leaders, architects, or full-stack AI engineers.
Partner with Talent Acquisition and Human Resources to convert relationships into hires, and act as a credible ambassador for New York Life's AI mission and engineering culture.
Build, lead, and develop the West Coast AI engineering and solution delivery team; set performance expectations and retain talent in a highly competitive market.
Own the AI capability uplift for New York Life's existing engineering population: curriculum, enablement materials, hands-on labs, embedded pairing and rotations, internal certification, and a community of practice anchored in the AI CoE's lifecycle and platform standards.
Make upskilling measurable — practitioners trained and certified, teams able to deliver on the platform without hands-on CoE support, and reduced dependency on external vendors for AI delivery.
Own delivery end to end for the in-house AI solutions this team builds: business case, architecture, build, deployment, adoption, and measured outcome.
Partner with business units and with AI, Data, and Technology leadership to define the business case for each solution — value hypothesis, KPIs, funding, and adoption plan — and remain accountable for value realization after deployment.
Ensure in-house solutions are built on and strengthen shared AI CoE capabilities and reusable assets rather than accumulating as one-off point solutions.
Hold the three pillars accountable to delivery, reporting on process, platform, and people in terms of their effect on production outcomes.
Provide senior AI leadership coverage in the Pacific time zone for high-priority initiatives involving West Coast providers, partners, and engineering resources outside East Coast business hours.
Serve as an escalation point for time-sensitive AI matters arising late in the East Coast business day (NYL AI production support, etc), with a documented escalation path into Home Office AI leadership.
Maintain strong working alignment with Home Office AI leadership despite time-zone separation and represent Home Office positions accurately on the West Coast.