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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...
The Director, Cloud & AI Infrastructure Operations will be a senior technical leader responsible for the reliability, security, and cost-efficiency of Ford Credit's cloud and container-based application landscape, with a growing focus on AI infrastructure and secure AI enablement. This individual will operate at the intersection of cloud engineering, platform operations, cybersecurity, and emerging AI technologies — partnering closely with application teams, enterprise architecture, cloud platform teams, security engineering, and external technology partners.
Ford Credit's technology estate is increasingly hybrid — spanning Google Cloud Platform, containerized platforms (Kubernetes/OpenShift), traditional Ford data centers, and a rapidly growing footprint of AI-driven capabilities (LLMs, RAG pipelines, agentic automation). This Director will own the operational health, security posture, and cost efficiency of that hybrid estate, while also building Ford Credit's capability to safely and effectively adopt AI infrastructure and AI-assisted engineering tools. The role is both a hands-on technical leadership position and a strategic partner to the VP, translating emerging cloud/AI capabilities into governed, production-ready, cost-conscious solutions.
What you'll do...
Cloud & Container Operations
- Provide operational leadership and support for applications running on Google Cloud Platform (GCP) and on container operating systems such as Kubernetes and/or OpenShift.
- Maintain and evolve deep working knowledge of GCP services, GCP networking constructs, and secure architecture patterns; guide teams in designing and implementing secure solutions within GCP.
- Lead the design and implementation of High Availability (HA) and Disaster Recovery (DR) infrastructure for applications spanning Google Cloud and Ford data centers, ensuring resiliency across hybrid environments.
AI Infrastructure & Innovation
- Build and apply familiarity with AI infrastructure concepts — including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and Agentic AI solutions — to design and execute solutions that improve IT processes and drive automation.
- Develop working knowledge of AI-powered IDEs (e.g., Cursor, Claude) and promote their effective, secure use within engineering teams to boost developer productivity.
- Gain and apply working knowledge of Google AI services (e.g., Gemini Enterprise, AI Runtime, Vertex AI Search, Feature Store) to design and execute secure, production-grade AI solutions.
- Partner with security engineering to identify, evaluate, and implement security controls and solutions that protect AI architectures and AI agents from emerging threats.
Security & Compliance
- Maintain working knowledge of application and infrastructure security scanning tooling — including SAST, SCA, IaC scanning, DAST, and API security testing — and ensure these are embedded into cloud and container delivery pipelines.
- Ensure all cloud, container, and AI solutions adhere to Ford Credit and enterprise security, risk, and compliance standards.
Cost & Value Management
- Proactively identify and implement solutions to reduce infrastructure costs and AI token/consumption costs.
- Develop and present metrics and dashboards to management demonstrating the operational, financial, and productivity benefits of implemented solutions.
Collaboration & Leadership
- Collaborate extensively across internal Ford Credit teams (application, infrastructure, security, architecture) as well as external partners, vendors, and Ford enterprise technology teams.
- Mentor and develop technical talent within the team; act as a technical escalation point for complex cloud, container, and AI infrastructure issues.
- Represent Ford Credit's cloud and AI infrastructure strategy in cross-functional and leadership forums.
You'll have...
Required:
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience).
- Significant experience (typically 10+ years) in cloud infrastructure/platform engineering, with substantial hands-on experience on Google Cloud Platform.
- Proven experience operating and supporting applications on container platforms such as Kubernetes and/or OpenShift.
- Demonstrated experience designing and implementing secure cloud architectures, including GCP networking, IAM, and security controls.
- Experience designing and implementing HA/DR solutions across hybrid environments (cloud and on-premises data centers).
- Working knowledge of AI/ML infrastructure concepts (LLM, RAG, MCP, Agentic AI) and their practical application to IT operations and automation.
- Familiarity with AI-powered development tools (e.g., Cursor, Claude Code) and modern developer workflows.
- Working knowledge of Google AI/ML platform services (Gemini Enterprise, Vertex AI Search, Feature Store, AI Runtime, etc.).
- Understanding of security considerations unique to AI systems and AI agents (e.g., prompt injection, data leakage, model/agent access control).
- Working knowledge of security scanning tools/practices across SAST, SCA, IaC, DAST, and API security.
- Strong track record of cross-functional collaboration with internal and external stakeholders.
- Demonstrated ability to build business cases and present cost/benefit metrics to senior leadership.
- Strong leadership, communication, and people-management skills; experience leading technical teams.
Even better, you may have...
- Google Cloud certifications (e.g., Professional Cloud Architect, Professional Cloud Security Engineer).
- Kubernetes/OpenShift certifications (e.g., CKA, CKAD, Red Hat OpenShift certifications).
- Experience in financial services or a highly regulated industry.
- Experience with FinOps/cost optimization practices for cloud and AI workloads.
- Exposure to DevSecOps pipelines and integrating security scanning into CI/CD.
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.
- "The compensation for this position depends on the location of hire. The first salary range listed applies to most U.S. locations, while the second, higher range applies exclusively to candidates hired in California."
This position is leadership level 5 and ranges from $141,700-$268,300.
This position is a leadership level 5 and ranges from $148,800-$281,700.
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/LL5
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 $141K-$281K 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. Director-level AI roles across all categories have a median of $272,275. Disclosed range: $141K to $281K.
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.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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