Principal AI Engineer

New York Life•New York, NY

22h•$185,000 - $264,500•Hybrid

About The Position

As a Principal AI Engineer within the Artificial Intelligence & Data (AI&D) organization, you will serve as a senior technical leader responsible for designing, building, and delivering enterprise AI solutions that create measurable business value. This is a hands-on engineering role that spans traditional machine learning, Generative AI, and Agentic AI, requiring deep technical expertise across the full AI solution lifecycle—from opportunity assessment and solution architecture through deployment, monitoring, and continuous improvement. Working closely with business stakeholders, product managers, data scientists, software engineers, and technology partners, you will lead the delivery of production-ready AI solutions on New York Life's enterprise AI platform. Rather than developing the underlying platform infrastructure, you will leverage enterprise capabilities to rapidly deliver scalable, secure, and responsible AI applications that solve complex business problems. In addition to leading technical delivery, you will help define AI engineering standards, influence enterprise AI strategy, mentor engineers, and partner with the business to measure and maximize the value generated by AI investments.

Requirements

Master's or PhD in Computer Science, Artificial Intelligence, Engineering, Mathematics, or a related quantitative discipline.
Eight or more years of experience delivering machine learning and AI solutions that solve complex business problems, with increasing technical leadership and ownership.
Demonstrated expertise designing and delivering production AI applications spanning machine learning, Generative AI, and agentic AI architectures.
Strong software engineering skills in Python, with working knowledge of SQL and JavaScript or TypeScript. Experience building, testing, deploying, and supporting production software using modern engineering practices, including CI/CD, code review, Docker, and Kubernetes.
Deep hands-on experience with large language models, prompt engineering, retrieval-augmented generation (RAG), hybrid retrieval techniques, knowledge graphs, and production retrieval architectures.
Experience designing and implementing multi-agent AI systems using modern orchestration frameworks (such as LangGraph, Google ADK, CrewAI, or similar) and tool integration protocols such as MCP or equivalent approaches.
Experience evaluating AI systems using automated evaluation pipelines, safety testing, red teaming, hallucination detection, performance monitoring, and responsible AI practices.
Experience deploying AI solutions on enterprise cloud platforms, particularly Google Cloud Vertex AI, with familiarity across AWS AI services such as Bedrock and SageMaker, as well as modern data platforms including Databricks.
Experience using AI-assisted software engineering tools (such as Codex, Claude Code, or similar) to improve developer productivity, including developing engineering agents that automate software delivery workflows.
Excellent communication, stakeholder management, and collaboration skills with the ability to influence technical direction and communicate effectively with both technical and business audiences.
Demonstrated ability to mentor engineers, lead technical initiatives, and drive engineering excellence across teams.

Nice To Haves

Experience within life insurance, financial services, or other highly regulated industries.
Experience defining enterprise AI architecture, engineering standards, or AI governance frameworks.
Experience leading cross-functional AI transformation initiatives across multiple business domains.
Contributions to the AI community through publications, open-source projects, conference presentations, or technical thought leadership.

Responsibilities

Lead the end-to-end delivery of AI and Generative AI solutions, partnering with business, product, technology, and data teams to identify opportunities, shape solution strategies, and deliver measurable business outcomes.
Design, develop, deploy, and operate production AI applications using the enterprise AI platform, leveraging existing CI/CD pipelines, infrastructure templates, security controls, and platform services.
Architect and implement advanced AI solutions, including agentic AI systems, multi-agent orchestration, retrieval-augmented generation (RAG), hybrid retrieval strategies, and human-in-the-loop workflows to address complex business challenges.
Evaluate, select, and integrate AI technologies across the solution stack, including foundation models, orchestration frameworks, cloud AI services, and modern software engineering tools, ensuring solutions are scalable, secure, and maintainable.
Establish engineering best practices for AI development, including evaluation frameworks, automated testing, observability, responsible AI, safety validation, and production monitoring.
Rapidly prototype AI-enabled applications and user experiences to validate concepts, accelerate adoption, and inform product direction using modern development frameworks.
Leverage AI-assisted software development tools to accelerate engineering productivity while maintaining high standards for code quality, testing, and governance. Develop internal engineering agents that improve software delivery through automation of testing, code review, deployment validation, and other engineering workflows.
Provide technical leadership, mentorship, and architectural guidance to AI engineers and data scientists, fostering a culture of innovation, experimentation, technical excellence, and continuous learning.
Partner with business leaders to define success metrics, measure solution performance, and continuously optimize AI solutions to maximize business value.
Stay current on emerging AI technologies, evaluate their applicability to enterprise use cases, and influence the organization's AI roadmap and engineering practices.