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About This Role
Overview
At Ford, you’ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we’re shaping the next era of transportation—grounded in purpose, driven by progress. Make your move.
- Job Type: Full time
- Work Type: Remote
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 legacy systems through modular architectures, APIs, events, observability, and practical transition plans.
- Turn architectural decisions and lessons learned into reference architectures, golden paths, validators, templates, runbooks, and platform capabilities.
- Advise senior leaders and build alignment across engineering, product, cybersecurity, data, legal, privacy, risk, procurement, and operations.
- Manage and develop an assigned team of architects through clear priorities, delegation, coaching, performance management, and succession planning.
- Use enterprise-approved AI tools responsibly to support eligible architecture research, synthesis, prototyping, and drafting while preserving confidentiality and human accountability.
You'll have...
- A bachelor’s degree in computer science, engineering, information systems, cybersecurity, or a related field, or equivalent practical experience.
- Candidates should bring at least 12 years of progressive technology experience, including at least eight years in architecture roles and sustained responsibility for enterprise or cross-domain decisions.
- They must have experience architecting and operating enterprise AI/ML systems in production, including generative AI, large language models, retrieval-augmented generation, agentic workflows, evaluation, observability, and model lifecycle management.
- Candidates must also bring deep cloud architecture experience on Google Cloud or a comparable hyperscaler and strong security architecture expertise spanning Zero Trust, identity, authorization, secrets, APIs, threat modeling, data protection, and software supply-chain security.
- A strong understanding of AI safety and security risks is essential.
- The role requires a record of creating standards, reference architectures, roadmaps, and guardrails used across teams
- Fluency in modern software delivery and production operations
- Formal people-management experience
- The ability to communicate complex technical and investment trade-offs to both executives and engineers.
Even better, you may have...
- A master’s degree in a related discipline
- relevant Google Cloud professional certifications
- hands-on familiarity with Google Cloud AI, data, compute, security, and integration services.
- Especially valuable are experience transforming enterprise delivery practices around specifications and automated evaluation
- modernizing complex hybrid estates; working in financial services or another highly regulated environment; and taking proofs of concept through enterprise adoption.
- Strong systems thinking and an ability to convert local learning into reusable architecture and operating-model improvements are also preferred.
You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!
As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including:
- Immediate medical, dental, vision and prescription drug coverage
- Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
- Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
- Vehicle discount program for employees and family members and management leases
- Tuition assistance
- Established and active employee resource groups
- Paid time off for individual and team community service
- A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
- Paid time off and the option to purchase additional vacation time.
This position is leadership level 6 and ranges from $132,800-$250,800.
Final determination of salary grade will be based on candidate's skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.
For more information on salary and benefits, click here: https://fordcareers.co/LL6
Visa sponsorship is not available for this position.
Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.
#LI-Remote#LI-FordCredit #LI-MN1
Salary Context
This $132K-$250K range is above the median for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Ford Motor Company, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($191K) sits 14% below the category median. Disclosed range: $132K to $250K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Ford Motor Company AI Hiring
Ford Motor Company has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist. Positions span Dearborn, MI, US, Remote, US, Dearborn, MI. Compensation range: $192K - $268K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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