Principal AI Engineer

Caterpillar Caterpillar · Industrial · Chennai, Tamil Nadu +1

Principal AI Engineer at Caterpillar, focusing on developing and scaling AI-based applications for the manufacturing domain. The role involves leading implementation strategies, designing knowledge transfer, driving innovation, and collaborating with product teams to integrate AI features into digital products. Requires experience in deploying production AI solutions, applying GenAI for industrial optimization, and mastery of ML algorithms. Nice-to-have skills include manufacturing data experience, digital twin solutions, and familiarity with industrial platforms.

What you'd actually do

  1. Lead and deliver implementation strategies for State of the Art (Gen)AI-based Applications in manufacturing domain.
  2. Design and implement a Knowledge Transfer strategy to scale the own experience via a Growing and Ambitious AI Team.
  3. Drive technical innovation through boundary-pushing experimentation, while maintaining alignment with customer commitments and delivery expectations.
  4. Collaborate with product, engineering, and operations teams to design and integrate AI features into digital products.

Skills

Required

  • Python
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • JAX
  • Langchain
  • Langgraph

Nice to have

  • technical training
  • mentorship
  • consulting
  • manufacturing data
  • industrial protocols
  • plant systems
  • digital twin solutions
  • Nvidia Omniverse
  • Siemens
  • PTC
  • Dassault Systèmes
  • IoT
  • PLC
  • MES/ERP connectivity

What the JD emphasized

  • successfully designing, developing, and deploying AI/ML solutions in a production environment
  • deploying production AI solutions (MLOps) with robust data pipelines, monitoring, retraining, and scalability in real factory settings

Other signals

  • AI/ML solutions in a production environment
  • GenAI for generative design, simulation, predictive modeling, demand forecasting, or process optimization
  • MLOps with robust data pipelines, monitoring, retraining, and scalability in real factory settings