Corporate Vice President - Senior Full-stack AI Engineer

New York Life•New York, NY

12d•$147,500 - $211,000•Hybrid

About The Position

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. Unlike traditional engineering roles, this position spans both platform and product development. One sprint you may be enhancing AI lifecycle services, memory architectures, or control-plane capabilities; the next, you'll be delivering an end-to-end agentic solution that transforms an insurance business workflow. You'll work across the full AI engineering stack—from cloud infrastructure, AI platform services, and developer tooling to multi-agent orchestration, enterprise knowledge systems, retrieval, governance, and production operations. This is an opportunity to help define how enterprise AI is built, deployed, governed, and operated within a highly regulated organization. Our platform is built on Google Cloud and designed to leverage cloud-native services while remaining portable through open standards and reusable engineering patterns. Success in this role requires strong software engineering fundamentals, practical AI expertise, and a passion for building production-quality systems that create measurable business value.

Requirements

Strong software engineering experience with Python and experience in one or more additional languages such as TypeScript or Java.
Experience designing and building production AI platforms, enterprise software platforms, or cloud-native distributed systems.
Hands-on experience with modern generative AI technologies, including LLMs, multi-agent orchestration, retrieval-augmented generation (RAG), memory architectures, tool integration, and evaluation frameworks.
Experience with AI platform engineering, including model lifecycle management, agent runtimes, observability, developer tooling, and enterprise integration patterns.
Strong cloud engineering experience, preferably with Google Cloud Platform, including managed AI services, Kubernetes, networking, identity, containers, CI/CD, and Infrastructure as Code.
Experience implementing MLOps and LLMOps practices, including model deployment, evaluation, monitoring, tracing, performance optimization, and production operations.
Experience using AI-assisted software development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or similar technologies.
Strong communication and collaboration skills with the ability to work effectively across engineering, architecture, security, and business teams.
Ability to operate successfully within a regulated enterprise environment while balancing innovation with governance.

Nice To Haves

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

Responsibilities

Design, build, and enhance the enterprise AI platform, including model and agent lifecycle management, AI control-plane services, developer tooling, runtime orchestration, memory services, workflow management, and governance capabilities.
Build production-ready agentic AI solutions that solve complex business problems using multi-agent architectures, structured planning, tool integration, retrieval, memory, and human-in-the-loop workflows.
Design and implement enterprise knowledge systems using retrieval-augmented generation (RAG), knowledge graphs, semantic search, embeddings, and modern information retrieval techniques to improve agent performance and reasoning.
Develop secure, cloud-native AI infrastructure using Google Cloud Platform, Kubernetes, Infrastructure as Code, CI/CD, observability, and enterprise identity and access management while maintaining portability through open standards.
Implement MLOps and LLMOps capabilities, including model deployment, evaluation, observability, monitoring, cost optimization, runtime governance, testing, and safe release practices for production AI systems.
Partner with security, architecture, legal, and risk teams to embed responsible AI, governance, security, and compliance into platform capabilities and enterprise AI solutions.
Build platform capabilities as intelligent agents wherever appropriate, enabling the platform to automate lifecycle management, planning, governance, and operational workflows.
Leverage AI-assisted engineering throughout the software development lifecycle to accelerate delivery while maintaining high standards for quality, security, and reliability.