AI Product & GenAI Engineering

Unum•Portland, ME

1d•$133,500 - $274,100

About The Position

As a leader in advanced analytics and AI Product Engineering at Unum, this role helps transform the company from a traditional insurance provider into an AI-first digital insurance company. The position owns strategic AI-based products and offerings from solution design through enterprise-scale deployment, influences senior executives on AI strategy and investment, leads technical engineering teams, and supports enterprise-wide AI adoption and democratization. Working with AI Product Managers, Product Innovators, Data Scientists, architects, business leaders, and other functions, the role shapes technical solutions and advances AI opportunities from experimentation and MVP development to production at scale. It combines engineering leadership, solution design and architecture, AI-native development, and talent development, with an emphasis on Generative and Agentic AI, measurable business value, and balancing speed, scalability, security, maintainability, and business needs.

Requirements

Bachelor’s degree required; a background in Computer Science, Engineering, Math, Data Science, or Operational Research is specified.
10+ years of experience designing and building scaled AI/ML products or capabilities, including demonstrated experience taking Generative and/or Agentic AI solutions from experimentation through production and enterprise-scale deployment.
Excellent knowledge of computer programming, software engineering, DevOps/MLOps practices, solution design, and modern software architecture.
Demonstrated experience designing, deploying, and scaling agentic AI solutions across an enterprise or major business unit, from experimentation through production, adoption, and measurable impact.
Strong understanding of Generative AI and agentic architectures, including LLM integration, RAG, agent orchestration, evaluation, observability, and human-in-the-loop patterns, combined with experience designing modern cloud-native, API-driven solutions.
Experience transforming software development through AI-assisted coding, agentic development workflows, automated testing, CI/CD, and emerging engineering technologies.
Strategic innovation experience, including pushing boundaries and reimagining what is possible.
Execution experience taking ideas to scale, with a focus on speed-to-value, measurable outcomes, and continuous learning.
Ability to bridge theoretical and applied work and communicate the vision and experience to non-technical stakeholders.
Ability to take smart risks, encourage others to do so, and empower innovative approaches by motivating others to be proactive and resourceful.
Deep IT knowledge and diverse technical experience, including solution design and software architecture, with the technical credibility to guide Principal and Senior Engineers and make pragmatic decisions balancing speed, scalability, security, maintainability, and value.

Nice To Haves

Master’s degree preferred.
Experience with AWS or comparable cloud platforms, Python, Databricks, APIs and microservices, containers, event-driven architectures, CI/CD, automated testing, and modern AI and agent development frameworks.

Responsibilities

Provide input to and support execution of the advanced analytics and AI product engineering roadmap.
Lead AI-first digital products, offerings, and capabilities, including Generative and Agentic AI solutions.
Partner with Product Innovators and Managers, Data Scientists, architects, and business leaders to integrate and operationalize AI in business processes and digital products to deliver measurable business value.
Influence senior executives and business leaders on AI strategies and investments.
Lead and manage Software Engineering and Information Technology professionals working across agile, infrastructure, or operations teams and supporting the analysis, development, design, creation, modification, and testing of Unum AI products, software, and related applications.
Develop, coach, and engage team members to achieve higher performance.
Lead cross-functional and cross-disciplinary AI programs from experimentation and MVP development through scaled production deployment.
Advocate for architecture standards, practices, and processes, while providing technical and solution-design leadership that balances speed, scalability, security, maintainability, and business value.
Champion AI-native software engineering practices, including AI coding assistants, automation, automated testing, CI/CD, observability, and emerging development tools, to improve speed, quality, and developer effectiveness.
Support enterprise efforts to evangelize and democratize AI.