AI Integration Engineer

Littelfuse•Champaign, IL

About The Position

Littelfuse is seeking an AI Integration Engineer to serve as the technical expert and implementation lead for applying artificial intelligence across the Industrial Circuit Protection (ICP) Engineering team’s tools, processes, and data. Reporting to the Sr. Manager of Global ICP Engineering, this role identifies opportunities for AI, recommends practical solutions, and builds, deploys, and drives adoption of approved solutions. The goal is to help engineers work faster and with greater confidence in their data, improving system quality and accelerating the speed to market for new products. The position supports New Product Development and Introduction, Application Engineering, Project Management, and Sustaining Engineering, partnering with engineers across the global ICP team. The engineer will propose AI opportunities for the engineering roadmap, evaluate and recommend supporting infrastructure and tools, and implement approved processes for responsible and consistent AI use. Strategic direction, priorities, and final technology decisions remain with ICP engineering leadership. The role includes designing AI-enabled tools and automations that connect with existing engineering systems; improving the quality, structure, and reliability of engineering data; applying AI and machine learning to simulation, reliability analysis, test data review, and design validation; and supporting project management, documentation, reporting, knowledge sharing, root cause analysis, change management, and continuous improvement. It also evaluates and pilots AI platforms and tools, works with IT on security and governance, and trains engineers on approved tools and standards.

Requirements

Bachelor’s degree in Electrical, Mechanical, or Industrial Engineering, Computer Science, or a similar discipline.
5–10 years of experience in a product or process development engineering role.
Demonstrated passion for and track record of applying AI to improve engineering workflow, efficiency, or data quality.
Practical experience with generative AI and large language model tools incorporated into business workflows.
Proficiency in Python or a similar language for data analysis, automation, and tool development.
Understanding of the product development lifecycle, including design, validation, and launch of new products.
Experience working with engineering data from test, simulation, reliability, or manufacturing sources.
Flexibility and willingness to work with multiple functional groups within the company.
Strong problem-solving and analytical skills, with high attention to detail.
Strong communication skills and fluency in written and spoken English, including the ability to explain technical concepts.
Self-motivation and ability to work effectively in a fast-paced, international organization.
Proficiency in Microsoft Office products.
Willingness to travel occasionally to Littelfuse engineering sites.

Nice To Haves

Experience with machine learning frameworks, data pipelines, cloud AI platforms, or low-code automation tools, ideally applied to electrical products, circuit protection, or reliability engineering.
Master’s degree in Engineering, Data Science, Computer Science, or a similar discipline.

Responsibilities

Partner with engineering teams to identify opportunities where AI can improve workflow, efficiency, and decision making.
Propose use cases with clear business value, KPIs, and implementation plans for the AI roadmap, and lead execution of the approved roadmap.
Design, build, and deploy approved AI-enabled tools and automations that integrate with existing engineering systems.
Contribute technical expertise to the engineering data strategy and improve the quality, structure, and reliability of engineering data, including test results, design records, and specifications.
Apply AI and machine learning to accelerate simulation, reliability analysis, test data review, and design validation.
Use AI to streamline project management, documentation, reporting, and knowledge sharing across engineering.
Support sustaining engineering through AI-assisted root cause analysis, change management, and continuous improvement.
Evaluate and pilot AI platforms and tools and recommend options for approval; work with IT to support data security, governance, compliance, and responsible-use processes.
Recommend AI best practices, implement approved standards, and train and coach engineers on AI tools to drive adoption.
Lead delivery and adoption of approved AI initiatives, and measure and communicate their impact on system quality, engineering productivity, and speed to market for new products.