Principal AI Architect

We made history and now we work to transform the future – for our customers, our communities and our families. You’ll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.

The Ford Motor Credit Company team helps put people behind the wheels of great Ford and Lincoln vehicles. By partnering with dealerships, we provide financing, personalized service and professional expertise to thousands of dealers and millions of customers in over one hundred countries around the world.

In this position…

Ford Credit is seeking a Principal AI Architect to shape its enterprise AI strategy and turn it into secure, cloud-native, production-ready systems. This leadership-level player-coach will manage and develop AI, cloud, and security architects while directly owning selected high-impact architecture work. The role will establish reusable standards and delivery practices for AI-native software development, governed agentic systems, and technology modernization.

The Principal AI Architect provides enterprise direction for how Ford Credit designs, delivers, secures, and operates AI-enabled technology. Working across business and technology teams, this leader defines target and transition architectures, evaluates investment and risk trade-offs, and translates strategic decisions into practical roadmaps, reference architectures, standards, and implementation commitments. The role requires both executive-level judgment and the ability to test architectural assumptions against working systems and operational evidence.

A central focus is building an AI-native software delivery lifecycle that connects business intent and clear specifications to bounded execution, trustworthy verification, operational evidence, and continuous improvement. The Principal AI Architect also guides the design of production agentic systems, including their models, enterprise context, retrieval, tools, memory, orchestration, permissions, evaluations, observability, and recovery mechanisms. Security, human oversight, and auditable controls must be built into these systems, with autonomy matched to the consequences of each action.

The role guides cloud-native architecture and the modernization of legacy, mainframe, on-premises, SaaS, and cloud systems so they can support AI-assisted and agentic change safely. By converting recurring challenges into reusable golden paths and platform capabilities, the Principal AI Architect helps teams make sound decisions without creating a central approval bottleneck. Success includes an adopted AI architecture roadmap, production-ready agentic patterns, measurable improvements in delivery and risk management, and an architecture team with clear ownership and growing independent capability.

What you’ll do…

• Define Ford Credit’s multi-year enterprise AI architecture strategy, target states, transition roadmaps, standards, and investment priorities.
• Establish an AI-native software delivery lifecycle built around explicit specifications, bounded execution, automated validation, evidence, and learning loops.
• Create reusable patterns for architecture manifests, technical plans, contracts, acceptance criteria, authority boundaries, escalation rules, and evidence bundles.
• Architect production agentic systems that integrate models, retrieval, tools, skills, memory, state, orchestration, permissions, evaluation, observability, and recovery.
• Define risk-tiered autonomy, checkpoints, pause gates, human oversight, and auditable decision trails for consequential actions.
• Establish evaluation practices spanning automated and regression testing, adversarial testing, human review, user acceptance, and production outcome monitoring.
• Set patterns for authoritative enterprise knowledge, including provenance, freshness, retrieval, versioning, structured context, and durable memory.
• Design and govern scalable AI applications and platforms on Google Cloud, with appropriate use of services such as Vertex AI, Gemini, GKE, Cloud Run, BigQuery, and Pub/Sub.
• Embed Zero-Trust principles, identity and access controls, data protection, software supply-chain security, and incident response into AI and cloud architectures.
• Address AI-specific risks, including prompt injection, data leakage, tool misuse, excessive agency, insecure retrieval, poisoning, and unsafe generated code.
• Guide the modernization of hybrid and legac…