Job description

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. Enterprise Technology plays a critical part in shaping the future of mobility. If you’re looking for the chance to leverage advanced technology to redefine the transportation landscape, enhance the customer experience and improve people’s lives, this is the opportunity for you. Join us and challenge your IT expertise and analytical skills to help create vehicles that are as smart as you are. In this position... The Order Fulfillment Technology team is seeking a Senior AI Engineer to design, build, deploy, and operate production-grade AI solutions supporting forecasting, planning, and scheduling. You'll build retrieval-augmented generation applications and controlled AI agents that generate grounded recommendations and automation with human oversight. This is a hands-on role for someone who can move AI solutions beyond prototypes into secure, scalable, and trustworthy production services. What you'll do... • AI Application Development: Design and build production-grade LLM applications, RAG solutions, and tool-using AI agents to improve decision speed, accuracy, and transparency. • Retrieval Pipelines: Build retrieval pipelines covering ingestion, chunking, embeddings, hybrid search, reranking, grounding, and citations, producing recommendations that are traceable and earn user trust. • Engineering & Deployment: Develop maintainable Python services, APIs, and reusable AI components; deploy and operate them using automated testing, CI/CD, containerization, logging, and monitoring. • Evaluation & Monitoring: Create evaluation datasets and automated tests measuring task success, groundedness, safety, latency, and cost; monitor production for drift, failures, and cost anomalies. • Responsible AI & Governance: Apply responsible controls throughout the development lifecycle, exercising sound judgment on when generative AI is warranted versus a simpler, deterministic solution. • Stakeholder Partnership: Partner with Product, Software Engineering, Data Science, and Data Engineering to bring AI solutions from concept to production. • User Engagement: Spend time directly with planners, schedulers, and other end users to understand real workflows and pain points, ensuring AI solutions are grounded in how decisions are actually made. • Continuous Improvement: Evaluate emerging AI technologies based on measurable business value, quality, and cost. Establish reusable patterns the wider team can build on. You'll have... • Education: Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a related technical field, or equivalent experience. • Technical Experience: 5+ years in software engineering, ML engineering, or applied AI, including hands-on Python, SQL, and experience building or deploying LLM/agent-based applications (e.g., prompt engineering, fine-tuning, RAG, or frameworks such as LangChain, LangGraph, AutoGen, Semantic Kernel). • Software Engineering Practices: Experience developing maintainable production services/APIs, with working knowledge of automated testing, Git workflows, code reviews, CI/CD, and containerization. • Evaluation Discipline: Experience defining measurable quality criteria and evaluating AI/ML systems using quantitative metrics and structured human review. • Collaborative Delivery: Proven ability to work with software engineering teams and business users to move solutions from concept to production. • User-Centered Practice: Demonstrated experience engaging directly with end users to shape and validate AI solutions. • Communication: Proven ability to translate ambiguous business problems into technical solutions and interpret results for non-technical partners. Even better, you may have... • Advanced Degree: Master's degree or PhD in a quantitative or technical field. • Technical Domain Expertise: LLM application architectures (RAG, tool calling, multi-agent orchestration, memory management) and retrieval pipeline design (embeddings, chunking, hybrid search, reranking). • Agent Control & Orchestration: Structured outputs, workflow orchestration, agent state management, and human-in-the-loop controls. • Production Experience: Deploying and maintaining LLM/agent systems in production, including prompt versioning and CI/CD integration. • AI Observability: Implementing logging, tracing, dashboards, and cost/latency monitoring for model or agent behavior. • Security & Adversarial Testing: Testing for prompt injection, data leakage, unsafe tool execution, or unauthorized access. • Cloud & Model Infrastructure: Deploying LLM/agent solutions in a cloud environment (GCP preferred), with familiarity with vector databases and model hosting. • Industry Context: Familiarity with Automotive OEM data, dealer relationships, or supply chain logistics. 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 a salary grade 7 – salary grade 8 and ranges from $99,600-$192,900. 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. *Internal applicants: moving into this role may result in an adjustment to your current compensation based on the posted pay range for this role, taking into consideration your qualifications and other relevant factors. For more information on salary and benefits, click here: New Hire Benefits 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. This position is hybrid with a requirement to be onsite four or more days per week. #LI-Hybrid #LI-MG1

Requirements

Must-haves

In this position...
What you'll do...
AI Application Development: Design and build production-grade LLM applications, RAG solutions, and tool-using AI agents to improve decision speed, accuracy, and transparency.
Retrieval Pipelines: Build retrieval pipelines covering ingestion, chunking, embeddings, hybrid search, reranking, grounding, and citations, producing recommendations that are traceable and earn user trust.
Engineering & Deployment: Develop maintainable Python services, APIs, and reusable AI components; deploy and operate them using automated testing, CI/CD, containerization, logging, and monitoring.
Evaluation & Monitoring: Create evaluation datasets and automated tests measuring task success, groundedness, safety, latency, and cost; monitor production for drift, failures, and cost anomalies.
Responsible AI & Governance: Apply responsible controls throughout the development lifecycle, exercising sound judgment on when generative AI is warranted versus a simpler, deterministic solution.
Stakeholder Partnership: Partner with Product, Software Engineering, Data Science, and Data Engineering to bring AI solutions from concept to production.
User Engagement: Spend time directly with planners, schedulers, and other end users to understand real workflows and pain points, ensuring AI solutions are grounded in how decisions are actually made.
Continuous Improvement: Evaluate emerging AI technologies based on measurable business value, quality, and cost. Establish reusable patterns the wider team can build on.
You'll have...
Education: Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a related technical field, or equivalent experience.
Software Engineering Practices: Experience developing maintainable production services/APIs, with working knowledge of automated testing, Git workflows, code reviews, CI/CD, and containerization.
Evaluation Discipline: Experience defining measurable quality criteria and evaluating AI/ML systems using quantitative metrics and structured human review.
Collaborative Delivery: Proven ability to work with software engineering teams and business users to move solutions from concept to production.
User-Centered Practice: Demonstrated experience engaging directly with end users to shape and validate AI solutions.
Communication: Proven ability to translate ambiguous business problems into technical solutions and interpret results for non-technical partners.
Even better, you may have...
Advanced Degree: Master's degree or PhD in a quantitative or technical field.
Technical Domain Expertise: LLM application architectures (RAG, tool calling, multi-agent orchestration, memory management) and retrieval pipeline design (embeddings, chunking, hybrid search, reranking).
Agent Control & Orchestration: Structured outputs, workflow orchestration, agent state management, and human-in-the-loop controls.
Production Experience: Deploying and maintaining LLM/agent systems in production, including prompt versioning and CI/CD integration.
AI Observability: Implementing logging, tracing, dashboards, and cost/latency monitoring for model or agent behavior.

Nice-to-haves

Cloud & Model Infrastructure: Deploying LLM/agent solutions in a cloud environment (GCP preferred), with familiarity with vector databases and model hosting.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonci/cdmlsqlgcp

Platform: eightfold
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off

Parental Leave