Director - AI Engineering

Location Designation: Hybrid - 3 days per week

Technology, Data, AI And Ventures Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life’s businesses. We build solutions that power how we serve policy owners, agents, advisors and employees while delivering measurable business outcomes.

Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader, access to leading-edge technologies and the opportunity to help shape how a world-class financial services company competes in the AI era - all backed by the stability and purpose of a mutual company built to last.

Role Overview As a Director, AI Engineering 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.

What You’ll Do- 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. This role involves limited travel (<10%) to events and vendor meetings.

What You’ll Bring

• 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, …