Machine Learning and AI Developer

Ford’s Electric Vehicles, Digital and Design (EVDD) team is charged with delivering the company’s vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners.

In this role, you will be at the center of Ford's AI engineering capability, overseeing vendor fine-tuning operations, designing Ford's internal orchestration layer, and driving measurable improvements in AI engine performance across dealer service, manufacturing, and validation workflows.

Responsibilities

• Support Ford's AI and ML engineering capability within the TOP platform, including model fine-tuning oversight, agentic orchestration architecture, and LLM evaluation
• Oversee vendor fine-tuning of Google Cloud Vertex AI using Ford proprietary diagnostic data, ensuring compliance with Ford's IP protection requirements and model weight storage architecture
• Design and build Ford's Orchestration Layer, the integration framework that connects external AI engine with other Ford internal AI engines and TOP platform services
• Evaluate AI engine outputs against defined accuracy, latency, and first-time fix rate metrics; drive iterative improvement through structured feedback loops
• Define model evaluation frameworks and acceptance criteria for AI-generated triage recommendations, ensuring clinical accuracy before dealer-facing deployment
• Build internal Ford tooling for model monitoring, drift detection, and retraining triggers within Ford's GCP environment
• Collaborate with Ford's data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality
• Partner with the Ford GCP Cloud Engineers to ensure model artifact storage, versioning, and access controls comply with Ford's IP and security policies
• Contribute to the long-term insourcing roadmap by documenting model architectures, training pipelines, and prompt frameworks in sufficient detail to enable internal replication
• Represent AI and ML engineering in architecture reviews and vendor technical discussions.

Qualifications

• 5+ years of professional experience in machine learning engineering, AI systems development, or applied AI research
• 3+ years Hands-on experience fine-tuning LLMs in a cloud environment, with specific preference for Google Cloud Vertex AI or equivalent managed ML platforms
• 2+ years of experience building agentic AI systems using frameworks such as LangChain, LangGraph, Google Agent Builder, or equivalent orchestration tooling
• 4+ years of Proficiency in Python and ML development tooling including Hugging Face, PyTorch or TensorFlow, and MLflow or Vertex AI Experiments
• 3+ years of experience designing and evaluating LLM outputs for production systems, including prompt engineering, retrieval-augmented generation (RAG) architectures, and model evaluation metrics
• 5+ years of Strong understanding of MLOps practices including model versioning, deployment pipelines, monitoring, and retraining workflows on GCP
• 4+ years Experience working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns
• Strong written and verbal communication skills; ability to translate technical AI concepts for non-technical executive stakeholders