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Caterpillar Inc. · 1 day ago

Packaging Artificial Intelligence/Machine Learning Engineer

Morton, IL

Full-time

Onsite

Mid Level

$98K/yr - $146K/yr

Caterpillar Inc. is a global team focused on building sustainable communities and enabling customer success. The Packaging AI/ML Engineer will perform analytical tasks on large datasets to support data-driven business decisions, utilizing AI/ML technologies to enhance packaging engineering processes and operational efficiency.

ConstructionMachinery ManufacturingManufacturingMechanical Engineering

No H1B

Responsibilities

Directing the data gathering, data mining, and data processing processes in huge volume; creating appropriate data models

Exploring, promoting, and implementing semantic data capabilities through Natural Language Processing, text analysis and machine learning techniques

Leading to define requirements and scope of data analyses; presenting and reporting possible business insights to management using data visualization technologies

Conducting research on data model optimization and algorithms to improve effectiveness and accuracy on data analyses

Additionally, proficiency in query and database tools is essential for creating and testing queries, connecting to data warehouses, and analyzing results. Familiarity with advanced query features like sorting, filtering, and basic calculations ensures effective data handling

The role also involves implementing and supporting programming languages, writing and reviewing code, and following organizational standards for structured programming

Collects, cleans, and transforms raw data into usable formats for packaging‑related AI/ML applications

Builds pipelines for data ingestion and feature engineering to support scalable model development

Designs, trains, and optimizes machine learning or deep learning models relevant to packaging, such as defect detection, quality prediction, optimization modeling, or sustainability assessments

Experiments with algorithms and frameworks such as TensorFlow, PyTorch, and Scikit‑learn

Packages models into APIs or microservices for seamless integration with packaging systems or business platforms

Deploys AI/ML models into cloud, edge, or on‑premises environments to support real‑time packaging line insights and automation

Builds user interfaces or dashboards for visualizing AI outputs or interacting with deployed models

Handles backend logic and integrates AI services with core packaging engineering or business applications

Tracks and evaluates model performance post‑deployment to ensure stability and accuracy

Implements model retraining strategies, error‑handling mechanisms, and scalability improvements

Qualification

Machine LearningPythonData EngineeringAI/ML FrameworksCloud PlatformsQueryDatabase ToolsProgramming LanguagesAnalytical ThinkingAccuracyRequirements AnalysisAttention to Detail

Required

Accuracy and Attention to Detail: Understanding the necessity and value of accuracy; ability to complete tasks with high levels of precision

Analytical Thinking: Knowledge of techniques and tools that promote effective analysis; ability to determine the root cause of organizational problems and create alternative solutions that resolve these problems

Packaging (AI/ML and Data-Driven Development): Practical knowledge in machine learning, programming, and/or data querying to enhance packaging operations. Key skills include applying ML techniques for business improvements, using Python and/or R for model development, and performing data mining and cleaning for accurate analysis

Preferred

Business Statistics: Knowledge of the statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical tools and processes to assist in making business decisions

Machine Learning: Knowledge of principles, technologies and algorithms of machine learning; ability to develop, implement and deliver related systems, products and services

Programming Languages: Knowledge of basic concepts and capabilities of programming; ability to use tools, techniques and platforms in order to write and modify programming languages

Query and Database Access Tools: Knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use

Requirements Analysis: Knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non-functionality requirements to ensure the success of a system or software development project

Programming: Python, R, JavaScript, Java

ML/AI Frameworks: TensorFlow, PyTorch, Scikit‑learn

Data Tools: SQL, Spark, Pandas

DevOps/MLOps: Docker, Kubernetes, CI/CD Pipelines

Front‑End (for UI Development): React, Angular

Cloud Platforms: AWS, Azure, Google Cloud Platform (GCP)