AI Engineer

Job Title: Chief Engineer, AI Product Creation
Company: Ford Motor Company
Location: Dearborn, Michigan (On-site / Hybrid)
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Position Overview
As the Chief Engineer, AI Product Creation, you will lead the strategic integration of next-generation digital tools, machine learning, and advanced analytics across Ford’s Global Product Development System (GPDS). In this high-impact executive role, you will modernize engineering workflows, optimize historical data assets, and drive the transition toward a digital-first product creation lifecycle.
Key Responsibilities
Tactical Optimization (Immediate): Identify high-waste physical bottlenecks across GPDS processes; deploy AI accelerators (LLM coding assistants, automated tracking tools); roll out generative engineering and advanced simulation pilots.
System Integration (Near-Term): Architect a cross-functional data lake for real-time analysis; scale digital twin models and predictive simulations; optimize long-lead manufacturing tooling designs.
Digital Transformation (Long-Term): Lead the transition to a compressed, digital-first development cycle and foster an engineering culture equipped with generative design and AI capabilities.
Key Qualifications
AI & Technical Leadership: Proven track record deploying AI, machine learning, or high-fidelity simulation frameworks (e.g., PINNs, NVIDIA Omniverse, Siemens Tecnomatix) within physical engineering environments.
Execution: Strong ability to bridge the gap between software engineers/data scientists and traditional mechanical engineers, driving cultural and technological change.
Technical Skills:
Programming Languages: Proficiency in Python (required) and familiarity with languages such as C++, Java, or TypeScript/JavaScript.
Frameworks & Libraries: Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or Hugging Face.
Generative AI & LLMs: Experience working with Large Language Models (LLMs), prompt engineering, RAG (Retrieval-Augmented Generation), vector databases (e.g., Pinecone, Weaviate, Qdrant), and frameworks like LangChain or LlamaIndex.
Cloud & Infrastructure: Experience deploying solutions on cloud environments (AWS, GCP, or Azure) using containerization tools like Docker and Kubernetes.
Data Management: Proficiency with SQL and NoSQL databases, as well as big data technologies or data processing tools (e.g., Pandas, Spark).

Work arrangement
Yes