Applied AI Engineer

**Applied AI Engineer – EV Powertrain
**Location: Lausanne, Switzerland or Cambridge, UK

_About the role
_Neural Concept's Vertical Solutions team builds data-driven workflows for specific engineering applications that sit on top of the Neural Concept platform. Working alongside the Solution Owner — who defines product direction and customer value — you bring the deep engineering domain expertise and hands-on technical build capability needed to turn that direction into a working solution. You combine real-world EV Powertrain design experience with fluency in modern AI tooling, including foundation models and LLM-based build assistants, to prototype and ship production-grade workflows quickly.

What you will do

• Build data-driven engineering workflows for EV Powertrain applications (electric motor, gearbox, battery, EDU) on the Neural Concept platform, translating the Solution Owner's product direction into working solutions.

• Work with lead customers to understand and solve their challenges

• Apply deep domain knowledge to ensure workflows reflect real engineering practice, constraints, and edge cases that a non-domain builder would miss.

• Hands-on prototyping using AI/ML tooling — foundation models, LLM-based coding assistants (e.g. Claude), and Python/CAE scripting — to build and iterate rapidly.

• Work day-to-day with CAD/CAE/ML engineers to take prototypes from first pass to production-ready quality.

• Validate outputs against real-world engineering standards, using your domain judgment as the quality bar.

• Feed technical and domain insight back into the broader roadmap based on what you learn while building.

Who you are

• 5+ years of hands-on engineering experience at an OEM or Tier 1 supplier, designing electric motors, gearboxes, batteries, or other EDU sub-systems — system-level experience preferred.

• Fluent in Powertrain simulation tools (Motor-CAD, Maxwell, JMAG, Romax) and/or system-level tools (MATLAB/Simulink, Amesim, or in-house tools).

• Proficient, hands-on user of modern AI tooling — comfortable building real engineering solutions with foundation models and LLM-based tools such as Claude or Codex, not just casually experimenting with them.

• Direct involvement in an AI transformation initiative at your current or previous company — evidence you've driven AI adoption in engineering practice, not just observed it.