Job Overview

The AI Transformation Engineer - Engineering Data & Automation is responsible for delivering AI-enabled engineering solutions that leverage engineering data, simulation, CAD/CAE environments, automation, and advanced digital capabilities. The role focuses on high-complexity initiatives requiring structured engineering data, workflow integration, automation, or technical coordination across multiple systems.
This position leads the technical implementation of assigned AI initiatives from assessment through pilot, validation, deployment, and adoption.

KEY RESPONSBILITIES:

• •    Identify opportunities to leverage AI within engineering data, simulation, CAD/CAE, design automation, and technical workflows.
  •    Evaluate data availability, data quality, integration requirements, and automation opportunities.
  •    Collaborate with AI specialists, digital teams, and engineering subject matter experts to define solution approaches.
  •    Define technical requirements and deployment strategies for approved initiatives.
  AI Solution Development and Deployment
  •    Develop prototypes and technical proof-of-concepts to validate solution feasibility before committing significant development resources.
  •    Lead deployment of AI-enabled engineering solutions involving data integration, workflow automation, and technical systems.
  •    Coordinate with AI Hub, DD&T, external development resources, and technical specialists as required.
  •    Development and deployment of AI agents, workflow automation solutions, and engineering productivity tools.
  •    Drive validation, testing, and deployment activities.
  Engineering Automation & Enablement
  •    Promote adoption of AI-enabled engineering methods.
  •    Identify opportunities to improve engineering efficiency through automation and reusable solutions.
  •    Coach engineering teams on practical AI applications within engineering workflows.
  •    Support development of scalable approaches that may be leveraged across multiple engineering teams.
  Experience & Qualifications
  •    Bachelor's degree in Engineering, Computer Science, or related discipline.
  •    Hands-on experience with one or more engineering technology domains such as CAD, CAE/simulation, PLM, engineering data management, or digital engineering workflows.
  •    Demonstrated hands-on experience with Python scripting and engineering workflow automation.
  •    Practical experience applying AI, automation, or digital technologies to engineering or business problems; experience with generative AI, LLMs, AI agents, or workflow automation preferred.
  •    Assess system interfaces, APIs, data access, integration dependencies, and technical constraints.
  •    Familiarity with engineering data structures and digital engineering workflows.
  •    Strong analytical and problem-solving skills.
  •    Passion for AI-enabled engineering transformation

DESIRED SKILLS:

•    Bachelor's degree in Engineering, Computer Science, or related discipline.
•    Hands-on experience with one or more engineering technology domains such as CAD, CAE/simulation, PLM, engineering data management, or digital engineering workflows.
•    Demonstrated hands-on experience with Python scripting and engineering workflow automation.
•    Practical experience applying AI, automation, or digital technologies to engineering or business problems; experience with generative AI, LLMs, AI agents, or workflow automation preferred.
•    Assess system interfaces, APIs, data access, integration dependencies, and technical constraints.
•    Familiarity with engineering data structures and digital engineering workflows.
•    Strong analytical and problem-solving skills.
•    Passion for AI-enabled engineering transformation.

What your background should look like

Generally requires Bachelors degree in appropriate field with a minimum of 5 years of progressively responsible professional level experience; Masters degree or higher or local equivalent may be preferred at this level

Competencies