Corporate Vice President - Enterprise AI Architect

New York Life•New York, NY

4d•$147,500 - $211,000•Hybrid

About The Position

As the Enterprise AI Architect, you will serve as the technical anchor for the platform and the most senior individual contributor on the team and also AI architect for AIGC. You will define the engineering patterns, reference architectures, and reusable platform capabilities that every AI solution built across the organization will inherit. This role is responsible for designing and building the platform's most complex components—including AI lifecycle registries, control-plane services, harness and memory capabilities, and the engineering foundations that enable teams to rapidly develop, deploy, and govern agentic AI solutions. You'll partner closely with the AI Platform Lead while providing technical mentorship across the engineering team through architecture reviews, engineering standards, and hands-on implementation. This is a deeply technical role for an engineer who enjoys solving difficult distributed systems problems across cloud infrastructure, platform engineering, AI runtimes, and software architecture. The platform is built on Google Cloud and leverages modern cloud-native capabilities while maintaining portability through open standards and reusable engineering patterns. Our team is intentionally small, senior, and highly technical. Regardless of title, every engineer is expected to build AI systems—and build with AI.

Requirements

Significant experience designing and building production-scale software platforms, developer platforms, AI platforms, or distributed systems in cloud-native environments.
Deep expertise in full-stack software engineering, including backend services, APIs, distributed systems, and modern engineering practices, with the ability to own systems through production operations.
Strong experience with cloud infrastructure and platform engineering, including Google Cloud Platform (preferred), Kubernetes, containers, Infrastructure as Code (Terraform), CI/CD, identity, networking, observability, and multi-tenant architectures.
Hands-on expertise building production generative AI and agentic systems, including LLM runtimes, retrieval-augmented generation (RAG), memory architectures, multi-agent orchestration, Model Context Protocol (MCP), agent-to-agent (A2A) communication, and evaluation frameworks.
Experience designing secure, governed AI platforms, including identity-aware access controls, runtime guardrails, policy enforcement, auditability, observability, and responsible AI practices.
Strong software engineering background with expert-level Python and experience with one or more additional languages such as Go, Java, or TypeScript.
Demonstrated experience using AI-assisted software development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or similar technologies as part of modern engineering workflows.
Proven ability to influence engineering direction through technical leadership, architecture reviews, mentoring, and engineering best practices.

Nice To Haves

Experience building enterprise AI platforms within financial services, insurance, or another highly regulated industry.
Experience with Vertex AI, LangChain, Google ADK, AutoGen, CrewAI, MLflow, OpenTelemetry, Ray, vLLM, DeepSpeed, Delta UniForm, Apache Iceberg, or related AI platform technologies.
Experience building reusable developer platforms, internal engineering frameworks, or platform engineering capabilities adopted across multiple teams.
Knowledge of enterprise AI governance frameworks including NIST AI RMF, ISO 42001, SOC 2, HIPAA, GDPR, or emerging AI regulations.

Responsibilities

Define and evolve the enterprise reference architecture and reusable engineering patterns that underpin every AI asset, including common registration schemas, semantic metadata, execution contracts, governance controls, and lifecycle management.
Design and build the platform's most technically challenging components, including AI lifecycle registries, AI control-plane services, model abstraction layers, enterprise memory services, retrieval capabilities, and reusable platform APIs.
Develop the engineering foundations that enable rapid delivery of agentic AI solutions, including reusable scaffolds, frameworks, builder agents, and multi-agent implementation patterns.
Deliver platform capabilities as intelligent agents where appropriate, enabling the platform to automate its own lifecycle management, planning, governance, and operational workflows.
Establish engineering standards for software quality, testing, CI/CD, Infrastructure as Code, observability, security-by-default, and AI-assisted software development across the platform team.
Drive platform portability through open standards, containerization, standardized telemetry, metadata, model serving, and cloud-native engineering practices that avoid unnecessary vendor lock-in.
Review complex designs and production code, mentor senior engineers, and help establish a culture of technical excellence across the organization.
Build production-quality software while leveraging AI-assisted engineering throughout the software development lifecycle.