Sr. Director, AI Engineering & Platform

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 organization: a visible owner and advocate who publishes the roadmap, brings people up the curve, and reports the adoption and business value the platform delivers.

* Current experience of production AI systems: agentic architectures (multi-agent orchestration, tool use, and protocols such as MCP), retrieval-augmented generation at scale, model serving, and the evaluation methods that make probabilistic systems behave reliably. Ability to accurately review the hardest parts of the build, not only direct them. Understanding of where low-code platforms, and emergent agentic systems add value.

* Experience in a software engineering foundation beneath the AI: distributed and event-driven systems, clean API design, cloud-native infrastructure (containers, orchestration, infrastructure-as-code), and CI/CD, with expert command of at least one production language (Python the common case) and command of the trade-offs that decide whether a service scales, holds its latency, and stays inside its cost envelope.

* Experience taking AI or ML platforms from architecture to production at enterprise scale and operating them: systems other engineers and real users depend on daily, with the reliability, observability, and security posture that demands, well beyond prototypes or proofs of concept.

* AI/MLOps and lifecycle depth: model and prompt versioning, automated evaluation and regression testing, monitoring and drift detection, and release discipline (canaries, rollback, governance gates) that keeps quality from decaying silently.

* AI-specific security and risk experience: threat modeling for prompt injection, data leakage, and agent action control, with controls built into the platform rather than treated as another team's problem.

* Engineering leadership at senior scope: Director level and above with prior Senior Director or equivalent, software engineering experience with deep AI and ML engineering talent management and development expertise. Experience leading engineers, architects, and engineering leaders, and accountability for build-versus-buy and vendor decisions that held up against measured value in a fast market.

* Stood up an AI platform or an Agent Factory-style delivery engine from low maturity, largely through internal talent.

* Hands-on depth with the approved stack: examples include: AWS Bedrock and AgentCore, Snowflake Cortex and Streamlit, Anthropic Claude, Microsoft 365 (Powerapps and/or Copilot Studio), and Git-based engineering workflows.

* Multi-provider experience with real command of portability, routing, and resilience against lock-in.

* Advanced evaluation and reuse practice for agents: eval set design, judge models, red-teaming, and statistical rigor, plus reusable agent components such as shared tools and skills that compound across use cases rather than being rebuilt each time.

* Productized internal AI that led to high-value, marginal outcomes for enterprise employee and operations use; reflecting leadership in digital transformation

* A record of platform adoption and advocacy: raised an organization's fluency on a platform, not only its spend.