AI Engineer

Ford Ford · Auto · Dearborn, MI +1 · Ford Next Businesses

AI Engineer at Ford responsible for the end-to-end development and deployment of AI/ML models and solutions, focusing on improving customer experience and business efficiency. Requires experience in the full ML lifecycle, MLOps, and cloud deployment.

What you'd actually do

  1. Design, develop, and implement AI/ML models and algorithms to solve complex problems
  2. Perform data preprocessing, cleaning, and feature engineering to prepare data for model training
  3. Train, evaluate, and tune various machine learning models
  4. Develop and maintain robust and efficient code using Python and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn)
  5. Document code, experiments, and results clearly and concisely

Skills

Required

  • Python
  • SQL
  • TensorFlow
  • PyTorch
  • scikit-learn
  • AWS
  • GCP
  • Azure
  • GitHub
  • Docker
  • CI/CD
  • unit testing
  • code reviews

Nice to have

  • Google Cloud Platform (GCP) services (Vertex AI, BigQuery, Dataflow)
  • cloud-native Python applications
  • Agentic AI Frameworks
  • Spark
  • Tekton
  • Terraform
  • algorithmic pricing engines
  • optimization models
  • Retrieval-Augmented Generation (RAG)
  • semantic search architectures
  • machine data transformation
  • event logs analysis

What the JD emphasized

  • 3+ years of experience building and implementing models using AI/ML frameworks and libraries to solve practical business problems and deliver measurable impact
  • Experience with the full machine learning lifecycle, including data preprocessing, feature engineering, and model evaluation
  • Deep experience using open-source data science technologies such as Python and SQL for data manipulation, analysis, and model development
  • Experience using Gen AI technologies
  • Strong understanding of machine learning algorithms, techniques, and frameworks
  • Proficiency in building and training models using frameworks such as TensorFlow, PyTorch, or Scikit-Learn
  • Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment
  • Experience deploying ML solutions on cloud platforms (AWS, GCP, Azure), including familiarity with cloud-based storage and processing
  • Experience using version control systems like GitHub for managing code repositories and collaboration
  • Understanding of containerization technologies like Docker for packaging machine learning models and deploying them in production
  • Experience in Software Engineering practices such as CI/CD, unit testing, and code reviews

Other signals

  • end-to-end development of AI products and services
  • implementing analytical and machine learning solutions
  • improve customer experience, increase revenue, and drive business efficiency at scale