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Principal AI Engineer
Caterpillar Inc.. Lead and deliver implementation strategies for state-of-the-art GenAI-based applications in the manufacturing domain .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in implementing GenAI-based applications within the manufacturing domain, focusing on data-driven decision-making, system integration, and technical innovation. Proficient in building scalable AI services and optimizing AI applications for performance and cost in industrial environments.
Highest-signal resume keywords
GenAI Application DevelopmentProduction-Grade AI ServicesPython ProgrammingMLOps DeploymentManufacturing Data Integration
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Artificial IntelligenceData ScienceMachine Learning AlgorithmsPredictive ModelingGenerative DesignLangChain FrameworkTensorFlowPyTorchScikit-learnJAX
Soft Skills
CollaborationMentorshipTechnical Training
Tools & Technologies
Digital Twin SolutionsKnowledge GraphsGraph DatabasesVector DatabasesIoT Integration
Industry Keywords
Manufacturing DomainData-Driven Decision-MakingReal-Time Data CollectionPredictive MaintenanceQuality Control
Tech Stack
Tools & technologiesERPIoTPythonPyTorchScikit-LearnTensorflow
About the role
Key responsibilities & impact- Lead and deliver implementation strategies for state-of-the-art GenAI-based applications in the manufacturing domain
- Design and implement a knowledge-transfer strategy to scale experience through a growing AI team
- Drive technical innovation through 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
- Contribute to system integration across manufacturing and supply systems, data sources, and workflows
- Enable data-driven decision-making through real-time data collection, analysis, and visualization
- Drive automation and optimization through intelligent scheduling, predictive maintenance, and quality control
- Enable collaboration across teams, functions, and geographies
Requirements
What you’ll need- 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 an associated field
- Alternatively, a Master’s degree and 8-10 years of experience
- Alternatively, a PhD and 5-7 years of experience in a relevant field
- Experience building production-grade AI services with scalability, availability, security, resiliency, performance, logging, monitoring, and cost optimization
- Experience with multimodal AI/RAG solutions involving text, tables, images, technical documents, diagrams, or enterprise/manufacturing data
- Experience applying GenAI to generative design, simulation, predictive modeling, demand forecasting, or process optimization in industrial environments
- Experience with frameworks such as LangChain, LangGraph, or industry GenAI stacks
- Mastery of supervised, unsupervised, and reinforcement learning algorithms applied to manufacturing
- Strong software engineering skills in Python and common ML libraries including TensorFlow, PyTorch, Scikit-learn, and JAX
- Experience deploying production AI solutions (MLOps) with robust data pipelines, monitoring, retraining, and scalability in real factory settings
- Prior experience in technical training, mentorship, or consulting preferred
- Experience with manufacturing data, industrial protocols, plant systems, and translating business goals into machine learning projects preferred
- Experience architecting or deploying digital twin solutions preferred
- Experience with knowledge graphs, graph databases, vector databases, manufacturing ontologies, and semantic models preferred
- Experience optimizing AI applications for latency, throughput, token consumption, infrastructure utilization, and operating cost preferred
- Knowledge of integrating real-time manufacturing data, including IoT, PLC, and MES/ERP connectivity, to digital twins preferred
Benefits
Comp & perks- Equal Opportunity Employer
- Talent Community opportunity