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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: Hybrid
As a Consulting Architect, you will define and drive enterprise architecture strategies that enable Ford's digital supply chain transformation. You will partner closely with business leaders, product teams, engineering organizations, and technology partners to deliver scalable, intelligent, and resilient supply chain solutions.
This role requires both strategic leadership and technical depth. You will shape architecture roadmaps, evaluate emerging technologies, guide solution implementation, and help engineering teams solve complex business and technical challenges. You will work across supply chain domains including Supplier Collaboration, Material Planning, Inventory Management, Logistics, Manufacturing Integration, Supply Chain Visibility, and AI-enabled decision support.
You will help establish the future-state architecture that connects business capabilities, data, analytics, AI, and operational systems to create an intelligent supply chain capable of anticipating disruptions, optimizing decisions, and accelerating business outcomes.
Enterprise Architecture & Business Transformation
- Develop and maintain enterprise architecture strategies aligned to Supply Chain business objectives and transformation priorities.
- Define future-state business capabilities, operating models, information architectures, and technology roadmaps.
- Lead architecture governance and solution reviews to ensure alignment with enterprise standards and strategic direction.
- Partner with business leaders to identify opportunities for modernization, simplification, automation, and innovation.
- Serve as a trusted advisor on technology investments, architecture decisions, and transformation initiatives.
AI-Enabled Supply Chain Architecture
- Architect intelligent supply chain solutions that enable sensing, prediction, recommendation, and autonomous action across supply chain operations.
- Design architectures that integrate operational systems, enterprise data platforms, analytics, machine learning, generative AI, and agentic AI capabilities.
- Define approaches for AI-powered planning, inventory optimization, logistics visibility, supplier collaboration, risk management, and operational decision support.
- Guide adoption of emerging technologies including Generative AI, Agentic AI, digital assistants, predictive analytics, and intelligent automation.
Data, Analytics & Integration Strategy
- Lead architecture for enterprise data products, semantic models, integration platforms, APIs, event-driven architectures, and analytical ecosystems.
- Establish scalable patterns for connecting SAP S/4HANA, Blue Yonder and other supply chain platforms, manufacturing systems, cloud services, and enterprise data platforms.
- Drive strategies that improve end-to-end visibility, data quality, governance, and business insights.
- Collaborate with engineering teams to enable real-time and near real-time decision-making capabilities.
Technical Leadership & Solution Delivery Support
- Partner directly with product, engineering, data, analytics, and platform teams to solve complex business and technical challenges.
- Lead architecture reviews and technical decision-making, balancing tradeoffs across scalability, performance, maintainability, security, and cost.
- Prototype and evaluate emerging technologies and architecture patterns to accelerate innovation.
- Provide technical leadership throughout the solution lifecycle, from concept and proof of value through production deployment.
- Mentor architects and engineers through design reviews, coaching, and collaborative problem-solving.
Cloud & Platform Architecture
- Define cloud-native architecture strategies leveraging Azure, Google Cloud Platform (GCP), and modern platform capabilities.
- Establish architectural patterns for APIs, microservices, event-driven solutions, data pipelines, analytics platforms, and AI workloads.
- Collaborate with platform teams to improve engineering productivity, operational resilience, and deployment velocity.
- Promote modern engineering practices, automation, and DevSecOps principles.
Security, Compliance & Governance
- Ensure architecture decisions align with enterprise security, privacy, regulatory, and compliance requirements.
- Embed security-by-design and responsible AI principles into solution architectures.
- Promote architecture standards, governance processes, and reusable patterns across the organization.
- Bachelor’s degree in computer science, Engineering, Information Systems, Supply Chain Management, or a related field.
- Master's degree preferred.
- 5+ Demonstrated experience in Automotive, Manufacturing Industry with a focus on Supply Chain Technologies and processes
- 5+ years proven experience in Enterprise Architecture, Consulting Architecture, Solution Architecture, or Technology Strategy leadership roles.
- Experience leading large-scale business and technology transformation initiatives.
- Strong understanding of enterprise architecture frameworks and methodologies.
- Deep knowledge of integration architecture, APIs, event-driven architectures, cloud-native design, and distributed systems.
- Experience with SAP S/4HANA and supply chain business processes.
- Experience designing data and analytics architectures using modern enterprise platforms.
- Experience architecting AI, machine learning, generative AI, or intelligent automation solutions in enterprise environments.
- Familiarity with modern AI engineering ecosystems, cloud AI services, and data science platforms.
- Ability to influence executive, business, and technical audiences.
- Strong facilitation, negotiation, and organizational leadership capabilities.
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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 available for this position.
Relocation assistance IS provided for this role.
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.
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Salary Context
This $132K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ford Motor Company, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($191K) sits 10% below the category median. Disclosed range: $132K to $250K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Ford Motor Company AI Hiring
Ford Motor Company has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager, AI Software Engineer. Positions span Dearborn, MI, Remote, US, Dearborn, MI, US. Compensation range: $166K - $281K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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