Corporate Vice President - Principal AI Engineer, Solution Delivery

About the role

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.

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.
• 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.

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

Qualifications

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

Skills

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

Benefits

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 here to 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.