Senior AI Engineer

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.

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.

• 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.

• 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 Lang Chain, Lang Graph, Auto Gen, 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.
• Communi…