Corporate Vice President, AI Enablement Manager

New York Life•Tampa, FL

3d•$130,000 - $190,000•Hybrid

About The Position

The Manager of AI Enablement leads the function responsible for accelerating the responsible adoption and delivery of artificial intelligence capabilities across New York Life Direct. The team partners with business and technology stakeholders to identify high-value opportunities, shape scalable solutions, and move AI capabilities from discovery and experimentation through production, adoption, and measurable business impact. The scope is centered on agentic AI, supported by generative AI, predictive AI and machine learning, and advanced analytics enablement. The Manager is accountable for a cohesive AI enablement strategy and roadmap that brings together solution architecture, hands-on engineering, technical program leadership, reusable technology capabilities, governance, operating practices, and organizational adoption. The Manager combines people leadership, technology strategy, program execution, and sufficient technical depth to guide direction and challenge solutions while empowering senior technical leaders to own architecture and implementation decisions. The role builds strong partnerships across business teams, Application Development, Solution Architecture, Data and Analytics, Security, Technology Risk, Infrastructure, and enterprise AI and technology organizations.

Requirements

Significant experience in technology leadership, software engineering, AI or data solution delivery, and end-to-end implementation of enterprise technology capabilities.
Demonstrated experience leading technical professionals, developing talent, managing performance, and building high-performing multidisciplinary teams.
Strong understanding of artificial intelligence, with primary depth in agentic AI and supporting knowledge of generative AI, predictive AI and machine learning, advanced analytics, and the business and technology patterns through which these capabilities create value.
Experience guiding enterprise AI solutions from ideation and architecture through engineering, integration, production deployment, monitoring, support, and continuous optimization.
Strong understanding of modern software architecture, cloud platforms, APIs and integration, data architecture, automation, and scalable application delivery.
Understanding of AI solution lifecycle practices including evaluation, testing, observability, model and agent monitoring, and operational management.
Strong understanding of responsible AI, security, privacy, data governance, technology risk, and enterprise control requirements for AI-enabled solutions.
Demonstrated ability to translate business objectives into a prioritized AI portfolio with clear value hypotheses, delivery roadmaps, outcome measures, and adoption strategies.
Experience leading complex cross-functional programs, managing dependencies and delivery risks, and working effectively with vendors and strategic technology partners.
Excellent leadership, communication, stakeholder management, facilitation, and influencing skills across executive, business, and technical audiences.
Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or equivalent practical experience.

Nice To Haves

Experience with enterprise AI and cloud platforms and services, particularly AWS-based AI and machine learning capabilities.
Familiarity with large language models, AI orchestration and agent frameworks, retrieval and grounding patterns, vector search, prompt and context engineering, and AI evaluation approaches.
Experience with advanced analytics, data science, predictive modeling, or machine learning enablement in addition to generative AI.
Experience in applying product management, portfolio management, or change management practices to emerging technology capabilities.
Experience driving AI transformation in financial services or another regulated enterprise environment.

Responsibilities

Define and execute the strategy, roadmap, operating model, and priorities for AI Enablement across NYL Direct.
Lead and develop a multidisciplinary team spanning AI solution architecture, AI engineering, technical program leadership, and advanced analytics and AI enablement.
Partner with business and technology leaders to identify, assess, and prioritize AI opportunities based on business value, feasibility, data readiness, operational impact, risk, and ability to scale.
Provide leadership for the end-to-end AI solution lifecycle, from opportunity discovery and prototyping through architecture, engineering, integration, production deployment, adoption, monitoring, and continuous improvement.
Guide the delivery of agentic AI solutions, with supporting use of generative AI, predictive AI and machine learning, and advanced analytics, that are integrated into business processes and enterprise technology ecosystems.
Establish reusable AI capabilities, reference architectures, design patterns, integration approaches, evaluation frameworks, and engineering practices that accelerate delivery and reduce duplication.
Ensure AI solutions align with enterprise standards for responsible AI, security, privacy, architecture, data governance, compliance, resiliency, supportability, and operational risk.
Establish lifecycle practices for AI evaluation, testing, observability, monitoring, optimization, and ongoing management of agent and model behavior in production.
Advance modern AI engineering practices, including software engineering discipline, cloud-native delivery, automation, prompt and context engineering, agent orchestration, and AI-specific quality engineering.
Provide portfolio and technical program leadership across AI initiatives, creating transparency into priorities, dependencies, capacity, risks, decisions, delivery progress, and expected outcomes.
Drive organizational adoption by establishing practical enablement, training, reusable guidance, communities of practice, and change strategies that help teams apply AI effectively and responsibly.
Establish metrics for AI adoption, time-to-value, solution quality, reliability, reuse, productivity improvement, financial and operational impact, and realized business value.
Evaluate emerging AI technologies, platforms, frameworks, and delivery approaches, balancing innovation with enterprise risk, maintainability, cost, scalability, and sustainable value.
Build strong partnerships with enterprise AI, data, architecture, cloud, security, and technology teams to leverage shared investments, align standards, and influence enterprise capabilities where appropriate.
Develop, coach, and manage team members with clear accountability, strong technical development, and a culture of experimentation, engineering excellence, responsible innovation, and measurable outcomes.