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Voya Financial

Financial services: retirement, investments, insurance

AVP – Applied AI & Engineering

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USA$171.3k - $214.1k /yr

About the Job

Requirements

  • A bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, Finance, Business, or a related discipline is required.
  • At least 10 years of experience in technology, data science, artificial intelligence, analytics, software engineering, or digital transformation is required.
  • At least 5 years of leadership experience managing technical teams, cross-functional initiatives, or enterprise-scale technology programs is required.
  • Experience delivering artificial intelligence, analytics, data science, automation, or software solutions into production environments is required.
  • A strong understanding of Generative AI, Agentic AI, AI engineering, intelligent automation, and modern software delivery practices is required.
  • Practical experience translating business problems into AI-enabled solution designs is required.
  • Ability to guide engineers and data scientists while engaging effectively with business stakeholders is required.
  • Strong communication skills to explain AI concepts, business value, and implementation tradeoffs to technical and non-technical audiences are required.
  • Demonstrated ability to drive adoption, influence across teams, and deliver measurable business outcomes is required.

Responsibilities

  • Ensure AI solutions are designed for scalability, consistency, extensibility, and business adoption across domains.
  • Promote successful AI solutions across Investment Management and Distribution and expand them into reusable capabilities for related business use cases.
  • Promote collaboration, knowledge sharing, and common practices between AI engineering, data science, and business technology teams.
  • Reuse common assets such as agents, prompt libraries, retrieval-augmented generation patterns, evaluation approaches, semantic search, application programming interfaces, and workflow automation.
  • Build stronger connectivity between domain-specific AI teams while preserving the business context each team requires.
  • Partner with leaders across investment research, portfolio management, operations, distribution, sales, marketing, and technology to identify practical AI use cases.
  • Translate ambiguous business challenges into scalable AI-enabled solutions.
  • Help business teams move beyond AI experimentation toward production-ready capabilities that improve how work gets done.
  • Drive the adoption of AI-enabled engineering practices across Investment Management technology teams.
  • Help technology teams use AI to improve software development, data engineering, testing, documentation, support, and operational productivity.
  • Promote reusable engineering patterns, accelerators, templates, and implementation approaches.
  • Identify opportunities to apply capabilities built by one team to other areas across Investment Management.
  • Reduce duplicated effort by turning successful AI implementations into reusable capabilities, shared services, and common engineering practices.
  • Encourage disciplined engineering practices for AI solutions, including versioning, testing, evaluation, monitoring, documentation, and production readiness.
  • Stay actively involved in AI solution design, architecture discussions, prototyping, and implementation planning.
  • Guide teams in applying modern AI technologies such as Generative AI, Agentic AI, retrieval-augmented generation, semantic search, AI agents, and intelligent automation.
  • Evaluate emerging AI tools, frameworks, and platform capabilities for practical use within Investment Management.
  • Mentor AI engineers, data scientists, and technology teams on scalable AI implementation practices.
  • Help teams balance speed, reliability, cost efficiency, maintainability, and business value.
  • Work closely with AI engineering leaders responsible for agentic platforms, multi-agent frameworks, runtime architecture, AgentOps, observability, AI security, and engineering standards.
  • Ensure AI engineering roadmaps remain aligned with Investment Management business priorities.
  • Help define how AI platforms and reusable capabilities can support multiple teams and use cases.
  • Promote scalable implementation approaches across platforms such as Microsoft AI Foundry, Microsoft 365 Copilot, Copilot Studio, Databricks, Snowflake Cortex, Azure AI Services, and related AI engineering frameworks.
  • Mentor developing AI engineers, data scientists, and technology professionals.
  • Help younger AI team members build stronger understanding of Investment Management business workflows.
  • Promote practical AI education, knowledge sharing, and communities of practice across Investment Management technology.
  • Foster a culture of experimentation, engineering excellence, business partnership, and measurable outcomes.
  • Help build a scalable Applied AI capability that can support growing demand across the organization.
  • Present AI opportunities, implementation plans, and business outcomes to technology and business leadership.
  • Develop clear business cases that connect AI investments to measurable productivity, efficiency, decision quality, and operational improvements.
  • Track adoption, value realization, reuse, and scalability of AI solutions.
  • Serve as a trusted advisor to business and technology stakeholders on practical AI adoption within Investment Management.

Desired Qualifications

  • Experience in Asset Management, Investment Management, Wealth Management, Financial Services, or another regulated business environment.
  • Experience supporting investment research, portfolio management, investment operations, distribution, sales, marketing, or client-facing business functions.
  • Hands-on familiarity with modern AI platforms and engineering tools such as Databricks Genie AI, Snowflake Cortex, Microsoft AI Foundry, Microsoft 365 Power Platform, Python, SQL, and GitHub Copilot or similar AI-assisted engineering tools.
  • Experience with Agentic AI, multi-agent architectures, retrieval-augmented generation, semantic search, prompt engineering, AI evaluation and monitoring, AI workflow automation, application programming interface and tool integration, and reusable engineering frameworks.
  • Experience leading or mentoring AI engineers, data scientists, software engineers, or technology teams.
  • Experience creating reusable technical capabilities, shared services, engineering accelerators, or common implementation patterns.

About the Company

Voya Financial

Financial services: retirement, investments, insurance

Voya Financial provides retirement planning, investment management, and insurance products for individuals, employers, and institutions, mainly in the United States. Its products help individuals save and grow wealth for retirement, while employers offer retirement plans and benefits within their workforce, and institutions receive tailored investment management. The company earns fees from investment management and premiums from insurance policies, creating recurring revenue from long-term client relationships. Voya differentiates itself by emphasizing corporate responsibility, inclusiveness, and ESG-aligned practices to attract clients and talent, rather than competing only on price or returns. Its goal is to help clients achieve financial confidence and security by offering a broad, client-centric suite of financial services to support stable, long-term financial well-being.

Company size
5,001-10,000
Company stage
IPO
Headquarters
New York City, New York
Founded
2014

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