Corporate Vice President - Principal AI Engineer, Solution Delivery

New York Life•New York, NY

22d•$147,500 - $211,000•Hybrid

About The Position

As a Principal AI Engineer in the Artificial Intelligence & Data (AI&D) organization, you will be a senior technical leader driving New York Life's AI transformation. This is a hands-on, full-stack AI role: you design, build, and deliver AI solutions end to end, across traditional ML, Generative AI, and Agentic AI, while shaping technical strategy and mentoring/managing a growing team. You will scope and deliver core AI solutions in close partnership with business stakeholders, data scientists, engineers, and product and technology partners, and help shape enterprise AI practices and standards, bringing deep expertise in AI solution delivery, responsible AI principles, and New York Life's technology ecosystem. You will own solution delivery on top of the enterprise AI platform, shipping agentic solutions end to end rather than building the underlying platform infrastructure. Additionally, you will partner with the business to assess and quantify the overall value delivered by the AI solution.

Requirements

Advanced degree (MS or PhD) in Computer Science, AI, Engineering, Mathematics, or a related quantitative field.
8+ years of experience applying data science, ML, and AI to real-world business problems, with progressive growth in scope, complexity, and influence.
Fluent with AI-assisted engineering tools, such as Codex, Claude Code, used daily as a force multiplier, with critical review of generated output. Experience building engineering agents that accelerate delivery itself, such as testing, code review, or deployment-validation agents, not just using AI tools to write code.
Full-stack AI fluency: demonstrated ability to work across the spectrum from statistical modeling and ML to LLM application development and agentic system design, including rapid prototyping (e.g., Streamlit, React) and owning solutions through to production.
Strong software engineering skills in Python; working knowledge of SQL and JavaScript/TypeScript sufficient to build and ship a solution's front end (e.g., React). Comfort with modern development practices including testing, code review, and CI/CD. Working fluency with containers (Docker) and Kubernetes-based deployment, sufficient to independently ship and operate a solution on the platform, not to build or own platform infrastructure.
Deep hands-on experience with LLMs, RAG architectures, and prompt engineering, including graph-based retrieval (GraphRAG, knowledge graphs) alongside traditional vector-based RAG. Hands-on experience with multi-agent and sub-agent orchestration frameworks (e.g., LangGraph, Google ADK, or equivalent) and MCP or equivalent tool-integration protocols, for production agentic systems.
Experience developing and evaluating AI systems rigorously: automated evaluation pipelines, red-teaming, hallucination detection, safety testing, and performance monitoring.
Proficiency with cloud AI platforms, particularly GCP Vertex AI for agentic development, with working knowledge of AWS services (SageMaker, Bedrock) and modern data platforms (Databricks). Comfort consuming cloud-native platform services (existing IaC templates, CI/CD pipelines, the AI gateway) to deploy and operate solutions.
Strong stakeholder engagement and communication skills: ability to scope initiatives, present to senior leaders, manage expectations, and drive alignment across business and technology partners.
Track record of mentoring and elevating technical talent.

Nice To Haves

Experience in life insurance, financial services, or other regulated industries is a plus.

Responsibilities

Lead AI/ML and GenAI initiatives end-to-end, in partnership with data, technology, product, and business teams. Scope and shape initiatives from opportunity identification and feasibility assessment through solution design, delivery, and stakeholder alignment.
Own end-to-end delivery for AI solutions, from build through evaluation, deployment, and production monitoring, shipping and operating them on the enterprise AI platform using its existing CI/CD, IaC templates, and gateway.
Design and implement agentic AI systems, including multi-agent orchestration, tool use, memory architectures, human-in-the-loop checkpoints, and safety guardrails. Build multi-agent solutions with sub-agents that plan, implement, validate, deploy, and log.
Design and deploy production-grade RAG and retrieval systems, including hybrid search, reranking, evaluation, and advanced retrieval patterns (agentic RAG, graph-enhanced retrieval) as appropriate.
Lead technical decisions across the AI stack: model selection, orchestration frameworks and tool-integration protocols (e.g., LangGraph, MCP, or direct API integration), cloud AI platforms (GCP, AWS), and data platform (Databricks) integration.
Rapidly prototype AI-powered applications and interfaces to validate ideas, test usability, and accelerate adoption, using modern frameworks (Streamlit, React, or similar).
Build with AI-assisted engineering tools daily (such as Codex, Claude Code) as a force multiplier, with critical review of all generated output. Build engineering agents that accelerate the team's own delivery, for example agents that assist with testing, code review, or deployment validation, not just use AI tools to write code.
Define and evolve technical standards for AI development, including evaluation frameworks, testing practices, observability, and responsible AI principles. Evaluate and integrate emerging AI capabilities (new foundation models, agent frameworks, AI-assisted development tools).
Provide technical leadership and mentorship to data scientists and AI engineers, fostering a culture of rapid experimentation, rigorous evaluation, and continuous learning.