Infor is hiring a Senior Director, AI Engineering & Platform to own the platform and the engineering practice that bring Infor’s internal AI to production. This is an accountable executive role for a leader with deep technical credibility: someone who sets the architecture and the standard, stays close enough to the build to earn a strong team’s respect, and is measured on what reaches production and performs, not on what gets prototyped. The leader runs our AI development engine, the Agent Factory, and is accountable first for its people: leading and growing the engineers and engineering leaders who turn business problems into ML and AI applications and agents that hold up at the scale of a global software company.

The leader also reads a fast-moving market and leads disciplined experimentation, committing only to tooling defensible against measured value, not vendor enthusiasm or passing trends. They operate as a principled entrepreneur: owning the platform and its features as a product, leveraging data products rather than rebuilding them, and staying a visible advocate who brings the organization up the curve and adapts as the business surfaces real needs. The role calls for the judgment and communication of an accountable executive in a Principle-Based Management environment: comparative advantage, a contribution-motivated mindset, intellectual honesty under disagreement, and the clarity to carry engineering reality to leaders who are not engineers. Their first priority, throughout, is to lead and develop talent.

Data & AI is Infor IT’s integrated capability for internal AI: Trusted Data Products, AI Engineering & Platform, and Analysis & Engagement. Scope is internal AI for Infor employees and operations, not customer-facing product AI. Operating principles: strong economic thinking is required to innovate well, but not at a pace that trades traction for well-placed strategic bets on platform, solutions, and assets; allocate talent by comparative advantage, optimizing the team’s output as a whole against clear time horizons, success criteria, and accountabilities; ship outcomes, not slides. Infor is actively investing in and scaling this capability through 2026 and beyond.

AI Engineering & Platform is the supply side of the capability: the platform, the Agent Factory, and the engineering practice that brings agents and solutions to production responsibly. The work compounds when prototypes transition cleanly into production and reference architecture is reused rather than rebuilt for each use case. The leader sets that operating model with practical frameworks that draw the highest sustainable contribution from the team, leading close to the work and inspiring through technical credibility, dynamic prioritization, and situational awareness.

• Lead and grow the team. Build, mentor, and develop the engineering leaders and developer talent of the Agent Factory; allocate the work by comparative advantage and hold it to clear time horizons and accountabilities.

• Own the AI platform end to end: architecture, engineering, AI/MLOps, agent tooling, and operations, on reference architecture reused across use cases rather than rebuilt for each. Specifically, Snowflake; M365 (PowerApps, Copilot Studio); Claude; AWS Agentcore.

• Set the technology direction Strategy informed by quality vendor relationship driven knowledge: own build-versus-buy and runtime decisions across the approved stack, with a clear read on switching costs and lock-in and the discipline to experiment before committing. Acquire and share relevant knowledge through regular engagement with providers and partners.

• Bring agents to production and keep them there: set the architecture and standards that turn prototypes into production by design, and run to a production standard with the reliability, monitoring, and evaluation that keep AI output dependable and accurate, not just shipped.

• Manage the economics: partner with Finance on FinOps for consumption-priced platforms, with cost centers, per-user and tier thresholds, and anomaly alerts that tie spend to value before it becomes a cost event.

• Own the AI risk posture with Legal, Security, Infrastructure, and Compliance: tool approval, vendor security, and regulatory readiness, so AI controls extend the enterprise security posture rather than run parallel to it.

• Carry the platform to the organ