Principal AI Engineer

Build the Digital Backbone of Modern Manufacturing

We’re assembling a dynamic team to develop and scale our Manufacturing & Supply Digital Platform—a next-generation software framework that transforms how manufacturing and supply operations connect, collaborate, and optimize.

This platform is not an ERP system. It’s a purpose-built digital layer that integrates data, processes, and resources across the entire manufacturing lifecycle—from design and engineering to production and distribution.

This initiative is powered by NVIDIA technologies, including the Omniverse platform and AI computing capabilities, enabling immersive digital twins, accelerated simulation, and intelligent automation. You’ll be part of a team that’s not just building software—but shaping the future of how manufacturing works through AI-driven, collaborative, and scalable digital solutions.

As part of this initiative, you’ll contribute to:

• System Integration: Seamlessly connecting diverse manufacturing and supply systems, data sources, and workflows into a unified digital ecosystem.
• Data-Driven Decision Making: Harnessing real-time data collection, analysis, and visualization to deliver actionable insights and operational intelligence.
• Automation & Optimization: Driving efficiency through intelligent scheduling, predictive maintenance, and quality control—without replacing core transactional systems.
• Enhanced Collaboration: Enabling transparent communication and coordination across teams, functions, and geographies.

If you're passionate about digital platforms, industrial innovation, and working with cutting-edge technologies—this is your opportunity to make a meaningful impact.

Key Responsibilities:

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

Required Skills:

• Demonstrated experience building production-grade AI services, including considerations for scalability, availability, security, resiliency, performance, logging, monitoring, and cost optimization.
• Experience with multimodal AI/RAG solutions involving combinations of text, tables, images, technical documents, diagrams, or other enterprise/manufacturing data.
• Application of GenAI for generative design, simulation, predictive modeling, demand forecasting, or process optimization in industrial environments. Experience with frameworks like Langchain, Langgraph or industry GenAI stacks.
• Mastery of supervised, unsupervised, and reinforcement learning algorithms as applied to manufacturing (predictive maintenance, defect detection, process automation).
• Strong software engineering skills in Python and common ML libraries (TensorFlow, PyTorch, Scikit-learn, JAX).
• Experience deploying production AI solutions (MLOps) with robust data pipelines, monitoring, retraining, and scalability in real factory settings.

Nice to Have Skills:

• Prior experience in technical training, mentorship, or consulting role, preferably within a corporate or academic setting.
• Demonstratable experience with manufacturing data, industrial protocols, plant systems, and translating business goals into machine learning projects.
• Architected or deployed digital twin solutions for virtual representation of physical assets/processes.
• Experience working with knowledge graphs, graph databases, vector databases, manufacturing ontologies, and semantic models to improve contextual retrieval and reasoning.
• Experience optimizing AI applications for latency, throughput, token consumption, infrastructure utilization, and overall operating cost.

• Knowledge of integrating real-time manufacturing data (IoT, PLC, MES/ERP connectivity) to digital twins for simulation and predictive analytics.

Educational Background: Typically requires a Bachelor’s degree, preferably in computer science, Artificial Intelligence, Data Science, mathematics, or a similar field with quantitative coursework, and 10-16 years of professional experience in associated field is required, a Master’s degree and 8-10 years of experience, or a PhD and 5-7 years of experience in relevant field.

Posting Dates:

October 6, 2026 - October 15, 2026

Caterpillar is an Equal Opportunity Employer. Qualified applicants of any age are encouraged to apply

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