Lead AI Architect: Enterprise Strategy & Secure Delivery

Ford Credit

United States

On-site

USD 133,000 - 251,000

Full time

4 days ago

Be an early applicant

Application generator

An application made for this job — a tailored resume and cover letter that speak straight to the posting.

Get past ATS filters

Benefits offered by this job

Immediate medical, dental, vision and药

Flexible family care days

Vehicle discount program for employees

Tuition assistance

Paid time off for community service

Generous paid holidays

Job summary

Ford Credit is seeking a Principal AI Architect to shape enterprise AI strategy and deliver secure, production-ready AI-enabled systems. This leader will manage AI/ML architects, define target architectures, and drive reusable patterns and governance across teams.

The role requires 12+ years in technology with 8+ in architecture, deep Google Cloud experience, and strong security focus. It offers executive exposure, a path to broad impact, and direct ownership of high-impact work within a major

Qualifications

Bachelor's degree in computer science, engineering, information systems, cybersecurity, or related field.
At least 12 years of progressive technology experience, including 8+ years in architecture and enterprise decisions.
Experience architecting and operating enterprise AI/ML systems in production (generative AI, LLMs, retrieval-augmented generation, agentic workflows).
Deep cloud architecture experience on Google Cloud or comparable hyperscaler; strong security architecture (Zero Trust, IAM, secrets).
Strong understanding of AI safety and security risks.
Proven record creating standards, reference architectures, roadmaps, and guardrails.
Fluency in modern software delivery and production operations.
Formal people-management experience.
Ability to communicate complex technical and investment trade-offs to executives and engineers.

Responsibilities

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 services like 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 architectures.
Address AI-specific risks, including prompt injection, data leakage, tool misuse, excessive agency, insecure retrieval, poisoning, and unsafe generated code.
Guide modernization of hybrid and legacy systems through modular architectures, APIs, events, observability, and transition plans.
Turn architectural decisions and lessons into reference architectures, golden paths, validators, templates, runbooks, and platform capabilities.
Advise senior leaders and align across engineering, product, cybersecurity, data, legal, privacy, risk, procurement, and operations.
Manage and develop an assigned team of architects with clear priorities and coaching.