AI Engineer

Ford Ford · Auto · Dearborn, MI +1 · Global Data Insight & Analytics

AI Engineer role focused on developing Generative AI and Agentic AI applications, traditional ML models, and RAG pipelines within Ford's Global Data Insight & Analytics department. The role involves architecting safe, observable, and robust solutions, integrating LLMs with statistical techniques, and deploying within cloud platforms (GCP/AWS/Azure). Responsibilities include designing AI agents, implementing ML models, developing RAG pipelines, ensuring safety and compliance, building monitoring, and adopting evaluation frameworks.

What you'd actually do

  1. Design and build state-of-the-art AI agents using modern orchestration frameworks (e.g., LangGraph, LangChain, Google ADK) to automate complex reasoning tasks.
  2. Design and implement supervised and unsupervised machine learning models (e.g., regression, classification, clustering) and statistical experiments to support data-driven decision-making.
  3. Develop advanced RAG (Retrieval-Augmented Generation) pipelines and API connectors to ingest and synthesize data from diverse sources, including internal databases, unstructured technical documents, and external third-party data.
  4. Implement robust safety layers and input/output validation using specialized frameworks (e.g., Google Model Armor) to prevent hallucinations, ensure data privacy, and maintain compliance.
  5. Build comprehensive monitoring pipelines using advanced evaluation tools (e.g., Arize Phoenix, Langfuse) to trace agent reasoning steps, track token usage, and monitor latency in production.

Skills

Required

  • Python
  • Generative AI/LLM applications
  • traditional Machine Learning
  • supervised/unsupervised learning
  • statistical modeling
  • Agentic workflows
  • tool use/function calling
  • GCP stack for AI/Data (Vertex AI, BigQuery)
  • LangGraph
  • LangChain
  • Google ADK
  • Scikit-learn
  • NumPy
  • Pandas
  • Matplotlib
  • TensorFlow
  • PyTorch
  • SQL
  • Vector Databases
  • APIs
  • data augmentation
  • observability frameworks
  • guardrail implementation
  • Model Context Protocol (MCP)
  • Vertex AI
  • Cloud Run
  • BigQuery

Nice to have

  • Master’s degree or PhD in a quantitative field
  • Ability to decompose complex business challenges into executable AI agent workflows and technical specifications
  • Excellent verbal and written communication skills
  • Strong skills in building relationships and collaborating effectively with stakeholders

What the JD emphasized

  • Agentic Workflows
  • Agentic AI applications
  • Agentic workflows (reasoning loops, tool use/function calling)

Other signals

  • Generative AI
  • Agentic Workflows
  • Machine Learning
  • Statistics
  • LLMs