AI Integration Engineer

Littelfuse•Champaign, IL

3d•$88,500 - $137,100

About The Position

Littelfuse is seeking an AI Integration Engineer to join the Industrial Circuit Protection (ICP) Engineering team. Reporting to the Sr. Manager of Global ICP Engineering, this role serves as the technical expert and implementation lead for applying artificial intelligence across engineering tools, processes, and data. The engineer identifies valuable AI opportunities, recommends practical solutions, and builds, deploys, and supports adoption of approved solutions. The role is intended for an experienced engineer who understands product development and is motivated to improve workflow, efficiency, and data reliability through AI. Success is measured by improved system quality and faster 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. It proposes opportunities for the engineering AI roadmap, evaluates and recommends supporting infrastructure and tools, and implements approved processes for responsible, consistent AI use. Strategic direction, priorities, and final technology decisions remain with ICP engineering leadership.

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 incorporating generative AI and large language model tools 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-motivated and able 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 AI roadmap use cases with business value, KPIs, and implementation plans, and lead execution of the approved roadmap.
Design, build, and deploy approved AI-enabled tools and automations that integrate with existing engineering systems.
Contribute 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 with AI-assisted root cause analysis, change management, and continuous improvement.
Evaluate and pilot AI platforms and tools, recommend options for approval, and work with IT on data security, governance, compliance, and responsible-use processes.
Recommend AI best practices, implement approved standards, and train and coach engineers to encourage adoption of AI tools.
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.