Lead Full-Stack AI Engineer

New York Life•New York, NY

2d•$124,000 - $177,000•Hybrid

About The Position

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 החדשנות 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. As a Lead Full-Stack Engineer, you will play a key role in shaping and evolving New York Life's enterprise AI platform while building scalable agentic AI solutions that drive business transformation. You will collaborate closely with business stakeholders to lead technical initiatives, shape architecture decisions, and mentor engineers, while remaining highly hands-on in the design, development, deployment, and operation of production AI systems.

Requirements

5-8+ years of software engineering experience.
Strong experience building enterprise platforms, cloud-native systems, or distributed applications.
Hands-on expertise with generative AI technologies, agentic architectures, RAG, memory systems, retrieval solutions, and enterprise AI applications.
Experience implementing MLOps and LLMOps capabilities in production environments.
Strong cloud engineering experience with Google Cloud Platform, Kubernetes, CI/CD, Infrastructure as Code, and observability tooling.
Proven ability to lead complex technical projects and influence architecture decisions.
Experience mentoring engineers and driving engineering best practices.

Responsibilities

Lead the design and implementation of enterprise AI platform capabilities, including agent runtimes, lifecycle services, orchestration frameworks, memory services, governance systems, and developer tooling.
Build and deliver complex agentic AI solutions using multi-agent architectures, knowledge systems, planning frameworks, and enterprise integrations.
Architect enterprise retrieval and knowledge management capabilities using RAG, knowledge graphs, semantic search, embeddings, and advanced retrieval approaches.
Design scalable cloud-native AI infrastructure using Google Cloud Platform, Kubernetes, Infrastructure as Code, observability frameworks, and enterprise identity services.
Lead implementation of MLOps and LLMOps practices including deployment, evaluation, monitoring, governance, cost optimization, and production operations.
Partner with architecture, security, legal, risk, and business stakeholders to ensure solutions meet enterprise standards.
Drive engineering excellence through architecture reviews, reusable frameworks, technical mentorship, and operational best practices.
Champion AI-assisted software engineering and help establish engineering patterns that accelerate delivery and quality.