AI Business Partner

Cigna Group•Bloomfield, CT

1d•Remote

About The Position

The AI Business Partner (AIBP) is a technical, AI-native product operator embedded with senior business leaders. This role identifies high-value business problems, architects AI solutions, and drives them from concept through early production. This is not a coordination or translation role. The AIBP operates comfortably across the executive table, solution architecture, and hands-on early-stage build work, requiring executive presence, product judgment, and real technical depth in AI-first development. AI is core to how top candidates work, already practicing spec-driven development, using agentic tools as standard, and capable of moving from executive problem framing to solution architecture to hands-on build.

Requirements

Demonstrated technical foundation with at least two of the following: Formal education in engineering, computer science, data science, or related field; Professional training in AI/ML; Hands-on experience building AI or data-intensive systems
Proven experience working in an AI-first way, actively using modern agentic development tools.
Strong product management or product engineering experience with the ability to move from executive narrative to detailed specs to shipped solutions.
Experience partnering with engineering and data science teams as a technical peer.
Financial and analytical acumen to assess value and execution risk.
Executive presence with credibility at both C-suite and CIO/engineering leadership levels.

Nice To Haves

Experience in enterprise product development, consulting, or software with significant AI/ML components.
Familiarity with LLM application design, enterprise AI architecture patterns, and responsible AI principles.
Track record of taking AI initiatives from POC to production in complex organizations.

Responsibilities

Partner with SBU and functional leadership to identify fundamental business problems with transformative AI potential.
Frame AI opportunities with financial, strategic, and technical rigor, enabling confident investment decisions.
Stack-rank AI initiatives based on value potential, feasibility, and execution risk.
Work directly with CIOs and technology leaders as a credible AI solution architect, not simply as a strategy or relationship lead, contributing to architectural decisions, trade-off analysis, and solution design.
Pattern-match business problems to existing AI solutions, reusable frameworks, and prior implementations.
Shape build vs. buy vs. adapt decisions with an enterprise-scale mindset.
Apply strong product thinking to define AI use cases with execution-ready depth.
Write clear specs, requirements, and acceptance criteria in direct partnership with engineering and data science teams.
Evaluate solutions for user value, viability, and iteration path — not just strategic fit.
Work at a product-builder level alongside engineering, SMEs, and operations to develop and iterate on POCs, prototypes, pilots, and early-stage production solutions.
Practice AI-first development as standard.
Spec-driven development and agentic tools like Cursor and Claude Code are operating procedure, not optional additions.
Guide business units through the AI lifecycle from ideation to value realization.
Partner with the PMO and AI Enablement teams to manage a prioritized AI portfolio and track value realization.
Communicate progress, risk, and impact to executive stakeholders.
Champion adoption of enterprise AI frameworks and best practices across business and technology teams.